Journal articles on the topic 'Spatial Hedonic Prices Method'
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Gong, Yunlong, and Jan de Haan. "Accounting for Spatial Variation of Land Prices in Hedonic Imputation House Price Indices: a Semi-Parametric Approach." Journal of Official Statistics 34, no. 3 (September 1, 2018): 695–720. http://dx.doi.org/10.2478/jos-2018-0033.
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Abstract Location is capitalized into the price of the land the structure of a property is built on, and land prices can be expected to vary significantly across space. We account for spatial variation of land prices in hedonic house price models using geospatial data and a semi-parametric method known as mixed geographically weighted regression. To measure the impact on aggregate price change, quality-adjusted (hedonic imputation) house price indices are constructed for a small city in the Netherlands and compared to price indices based on more restrictive models, using postcode dummy variables, or no location information at all. We find that, while taking spatial variation of land prices into account improves the model performance, the Fisher house price indices based on the different hedonic models are almost identical. The land and structures price indices, on the other hand, are sensitive to the treatment of location.
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Wang, Wen-Ching, Yu-Ju Chang, and Hsueh-Ching Wang. "An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City." ISPRS International Journal of Geo-Information 8, no. 6 (May 29, 2019): 249. http://dx.doi.org/10.3390/ijgi8060249.
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The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.
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Kuethe, Todd Henry. "International Real Estate Review." International Real Estate Review 14, no. 1 (April 30, 2011): 118–29. http://dx.doi.org/10.53383/100136.
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This study evaluates the ability of a range of popular aggregate house price indexes to predict house prices out-of- sample at the transaction level for a small geographic area. The analysis particularly addresses the utility of spatial econometric methods. The results suggest that spatial econometric methods, which more explicitly consider the spatial aspects of observed house prices, provide better predictive accuracy as compared to more traditional estimation techniques, such as the repeat sales index, a hybrid repeat sales-hedonic price index, and hedonic price models estimated through least squares. The conclusions are drawn from a sample of 38,984 single-family residential real estate transactions for the city of Milwaukee, Wisconsin over the years 2002-2008.
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Chwiałkowski, Cyprian, and Adam Zydroń. "Socio-Economic and Spatial Characteristics of Wielkopolski National Park: Application of the Hedonic Pricing Method." Sustainability 13, no. 9 (April 29, 2021): 5001. http://dx.doi.org/10.3390/su13095001.
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The study aimed to determine the influence of the proximity of Wielkopolski National Park (WNP) on the value of dwelling units in Mosina municipality. The research was conducted based on 1182 residential property transactions in the period from 2014 to 2018. The input data were subjected to spatial and statistical analysis. The main part of the analysis was performed with the use of the hedonic price method (HPM)—WLS (weighted least squares). The use of statistical tools made it possible to find undeniable evidence that the housing prices are positively related to the presence of Wielkopolski National Park, which has also been confirmed by previous research works conducted in other parts of the world.
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Kutasi, Dávid, and Milán Csaba Badics. "Valuation methods for the housing market: Evidence from Budapest." Acta Oeconomica 66, no. 3 (September 2016): 527–46. http://dx.doi.org/10.1556/032.2016.66.3.8.
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Different valuation methods and determinants of housing prices in Budapest, Hungary are examined in this paper in order to describe price drivers by using an asking price dataset. The hedonic regression analysis and the valuation method of the artificial neural network are utilised and compared using both technical and spatial variables. In our analyses, we conclude that according to our sample from the Budapest real estate market, the Multi-Layer Preceptron (MLP) neural network is a better alternative for market price prediction than hedonic regression in all observed cases. To our knowledge, the estimation of housing price drivers based on a large-scale sample has never been explored before in Budapest or any other city in Hungary in detail; moreover, it is one of the first papers in this topic in the CEE region. The results of this paper lead to promising directions for the development of Hungarian real estate price statistics.
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Mallios, Zisis. "Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment." International Journal of Information Systems and Social Change 1, no. 4 (October 2010): 1–13. http://dx.doi.org/10.4018/jissc.2010100101.
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Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.
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Good, Anthony, and Emily Pindilli. "Estimating the Effect of Tidal Marsh Restoration on Housing Prices: A Hedonic Analysis in the Nisqually National Wildlife Refuge, Washington, USA." Land 11, no. 9 (August 30, 2022): 1432. http://dx.doi.org/10.3390/land11091432.
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This study employs the hedonic pricing method and a rich spatial and temporal dataset from two counties in Washington, USA to determine the effect of the 2009 Nisqually Restoration project (NRP) on housing prices in adjacent communities. The NRP restored 308 hectares of wetlands via dike removal in the Billy Frank Jr. Nisqually National Wildlife Refuge (NNWR), leading to improvements in salmon and bird abundance and recreational opportunities. We find that the ecological improvements made by the NRP increased the value of homes within 0.5 mile of the refuge by $37,631; homes 0.5 to 1 mile by $10,489; and homes 1 to 1.5 miles by $31,186. Our findings are consistent with previous wetland hedonic price analyses and may be useful inputs in natural resource management and policy decision-making.
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Chica-Olmo, Jorge, Rafael Cano-Guervos, and Mario Chica-Rivas. "Estimation of Housing Price Variations Using Spatio-Temporal Data." Sustainability 11, no. 6 (March 14, 2019): 1551. http://dx.doi.org/10.3390/su11061551.
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This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.
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Lyu, Ying. "A Synthesis of Research on the Impact of Urban Rail Transit on the Price of Commercial Residences Along the Line." BCP Business & Management 29 (October 12, 2022): 427–34. http://dx.doi.org/10.54691/bcpbm.v29i.2307.
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While urban rail transit relieves traffic pressure and improves the urban transportation network, it also has a certain impact on the prices of commercial residential properties along the line, and more and more scholars have begun to pay attention to the relationship between rail transit and real estate prices. By sorting out the relevant domestic and foreign research, the existing research results are analyzed from three aspects: research objects, research methods, and research contents, and it is found that Hedonic Price Model is most commonly adopted, and the research in the fields of spatial effect, temporal effect and sub-market effect of rail transit on real estate prices is gradually becoming a hot spot.
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McCord, Michael James, John McCord, Peadar Thomas Davis, Martin Haran, and Paul Bidanset. "House price estimation using an eigenvector spatial filtering approach." International Journal of Housing Markets and Analysis 13, no. 5 (November 14, 2019): 845–67. http://dx.doi.org/10.1108/ijhma-09-2019-0097.
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Purpose Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity. Design/methodology/approach Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria. Findings The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error. Originality/value Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.
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Nam, Kichan, Ji-Wan Son, and Brian H. S. Kim. "Effect of Air Quality Improvements on Apartment Prices in Seoul: Application of Spatial Hedonic Price Model and Interpolation Method." Studies in Regional Science 43, no. 2 (2013): 163–75. http://dx.doi.org/10.2457/srs.43.163.
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McCord, M., P. T. Davis, M. Haran, D. McIlhatton, and J. McCord. "Understanding rental prices in the UK: a comparative application of spatial modelling approaches." International Journal of Housing Markets and Analysis 7, no. 1 (February 25, 2014): 98–128. http://dx.doi.org/10.1108/ijhma-09-2012-0043.
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Purpose – Accounting for locational effects in determining price is of fundamental importance. The demise of the mainstream property market has culminated in increasing appetite and investment activity within the private rental sector. The primary purpose of this paper aims to analyse the local variation and spatial heterogeneity in residential rental prices in a large urban market in the UK using various geo-statistical approaches. Design/methodology/approach – Applying achieved price data derived from a leading internet-based rental agency for Belfast Northern Ireland is analysed in a number of spatially based modelling frameworks encompassing more traditional approaches such as hedonic regressive models to more complex spatial filtering methods to estimate rental values as a function of the properties implicit characteristics and spatial measures. Findings – The principal findings show the efficacy of the geographically weighted regression (GWR) technique as it provides increased accuracy in predicting marginal price estimates relative to other spatial techniques. The results reveal complex spatial non-stationarity across the Belfast metropole emphasizing the premise of location in determining and understanding rental market performance. A key finding emanating from the research is that the high level of segmentation across localised pockets of the Belfast market, as a consequence of socio-political conflict and ethno-religious territoriality segregation, requires further analytical insight and model specification in order to understand the exogenous spatial and societal effects/implications for rental value. Originality/value – This study is one of only a few investigations of spatial residential rent price variation applying the GWR methodology, spatial filtering and other spatial techniques within the confines of a UK housing market. In the context of residential rent prices, the research highlights that a soft segmentation modelling approaches are essential for understanding rental gradients in a polarised ethnocratic city.
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Inagaki, Keiko, and Satoru Sadohara. "Spatial Analysis to Identify Factors Affecting Residential Land Prices in Disaster Areas." Proceedings of the ICA 2 (July 10, 2019): 1–4. http://dx.doi.org/10.5194/ica-proc-2-47-2019.
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<p><strong>Abstract.</strong> On March 11, 2011, a catastrophic earthquake struck Japan’s Tohoku area, which faces the Pacific Ocean. In this study, a multivariate spatial analysis was conducted to analyse the factors affecting residential land prices in the disaster-stricken area using the Hedonic Price Method. For the analysis, we first, collected spatial data, including land price maps, tsunami damage area map, flood hazard map, landslide hazard map, railway map, zoning map, and public building map of Miyagi prefecture. Second, we examined the extent to which the damage caused by the tsunami inundation can influence land prices, in order to clarify the relationship between natural disasters and land price fluctuations. The results of the multivariate analysis concluded that the tsunami inundation affected the price of land, particularly after the Great East Japan Earthquake (2011) in Miyagi prefecture. Furthermore, the degree of the tsunami inundation and the distance from public housing constructed after the disaster influenced land price fluctuations after the earthquake. In other words, the human settlements have been affected by disaster damage and reconstruction plan. Present studies have demonstrated the residents’ attitudes towards housing location choices before and after the earthquake disaster. By improving the precision of the multiple regression analysis in the future, we will be able to utilize the experiences of previous disasters as lessons learnt for safety and sustainability, particularly Sustainable Development Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable.</p>
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Amédée-Manesme, Charles-Olivier, Michel Baroni, Fabrice Barthélémy, and Francois des Rosiers. "Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach." Urban Studies 54, no. 14 (September 1, 2016): 3260–80. http://dx.doi.org/10.1177/0042098016665955.
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In this paper, the heterogeneity of the Paris apartment market is addressed. For this purpose, quantile regression is applied – with market segmentation based on price deciles – and the hedonic price of housing attributes is computed for various price segments of the market. The approach is applied to a major data set managed by the Paris region notary office (Chambre des Notaires d’Île de France), which consists of approximately 156,000 transactions over the 2000–2006 period. Although spatial econometric methods could not be applied owing to the unavailability of geocodes, spatial dependence effects are shown to be adequately accounted for through an array of 80 location dummy variables. The findings suggest that the relative hedonic prices of several housing attributes differ significantly among deciles. In particular, the elasticity coefficient of the apartment size variable, which is 1.09 for the cheapest units, is down to 1.03 for the most expensive ones. The unit floor level, the number of indoor parking slots, as well as several neighbourhood attributes and location dummies all exhibit substantial implicit price fluctuations among deciles. Finally, the lower the apartment price, the higher the potential for price appreciation over time. While enhancing our understanding of the complex market dynamics that underlie residential choices in a major metropolis such as Paris, this research provides empirical evidence that the QR approach adequately captures heterogeneity among house price ranges, which simultaneously applies to housing stock, historical construct and social fabric.
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Chwiałkowski, Cyprian, and Adam Zydroń. "The Impact of Urban Public Transport on Residential Transaction Prices: A Case Study of Poznań, Poland." ISPRS International Journal of Geo-Information 11, no. 2 (January 19, 2022): 74. http://dx.doi.org/10.3390/ijgi11020074.
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This study aims to determine the magnitude and nature of the impact of public urban transport accessibility on the value of residential properties in Poznań. The study was based on 2561 residential transactions completed within the study area in 2020. The input data obtained from the Board of Geodesy and Municipal Cadastre “GEOPOZ” were analysed statistically and spatially. The main part of both the spatial and the statistical analysis was performed using the hedonic pricing method (HPM)-OLS (ordinary least squares) and WLS (weighted least squares). The use of statistical tools enabled the finding of evidence to prove that the convenient accessibility of trams is positively related to housing prices. This has also been confirmed by previous research works conducted in other parts of the world. However, the collected data did not enable the identification of statistically significant relationships between housing prices and the distance from bus stops. The study also attempts to use spatial choropleth maps to clearly illustrate the mechanisms within the local housing market.
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Lin, Chien-Wen, Jen-Cheng Wang, Bo-Yan Zhong, Joe-Air Jiang, Ya-Fen Wu, Shao-Wei Leu, and Tzer-En Nee. "Lie symmetry analysis of the effects of urban infrastructures on residential property values." PLOS ONE 16, no. 8 (August 5, 2021): e0255233. http://dx.doi.org/10.1371/journal.pone.0255233.
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Due to the complexity of socio-economic-related issues, people thought of housing market as a chaotic nucleus situated at the intersection of neighboring sciences. It has been known that the dependence of house features on the residential property value can be estimated employing the well-established hedonic regression analysis method in teams of location characteristic, neighborhood characteristic and structure characteristic. However, to further assess the roles of urban infrastructures in housing markets, we proposed a new kind of volatility measure for house prices utilizing the Lie symmetry analysis of quantum theory based on Schrödinger equation, mainly focusing on the effects of transportation systems and public parks on residential property values. Based on the municipal open government data regularly collected for four cities, including Boston, Milwaukee, Taipei and Tokyo, and all spatial sampling sites were featured by United States Geological Survey (USGS) National Map, transportation and park were modelled as perturbations to the quantum states generated by the feature space in response to the environmental amenities with different spatial extents. In an attempt to ascertain the intrinsic impact of the location-dependent price information obtained, the similarity functions associated with the Schrödinger equation were considered to facilitate revealing the city amenities capitalizing into house prices. By examining the spatial spillover phenomena of house prices in the four cities investigated, it was found that the mass transit systems and the public green lands possessed the infinitesimal generators of Lie point symmetries Y2 and Y5, respectively. Compared statistically with the common performance criteria, including mean absolute error (MAE), mean squared error (MSE) and, root mean squared error (RMSE) obtained by hedonic pricing model, the Lie symmetry analysis of the Schrödinger equation approach developed herein was successfully carried out. The invariant-theoretical characterizations of economics-related phenomena are consonant with the observed residential property values of the cities internationally, ultimately leading to develop a new perspective in the global financial architecture.
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Wilhelmsson, Mats. "Energy Performance Certificates and Its Capitalization in Housing Values in Sweden." Sustainability 11, no. 21 (November 2, 2019): 6101. http://dx.doi.org/10.3390/su11216101.
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The impact on energy performance certificates on housing prices has been investigated extensively in recent years. However, the results of these investigations are mixed. We add to the literature by more specifically controlling for potential biases, by employing a combination of alternative approaches to estimate the causal relationship between house prices and energy performance certificates. We use a traditional hedonic modeling approach, but we additionally employ propensity score methods to be able to compare treated houses with a control group. We also investigate the impact of the outliers, spatial dependency, and parameter heterogeneity of our estimates. Moreover, we use the quantile regression technique to test the hypothesis that the capitalization effect varies across the price distribution. Our results, analyzing more than 100,000 observations, indicate there is an upward bias if one is not controlling for outlier and selection bias. Regardless of the propensity score method approach, the results are lower than a model (around 3 percent capitalization, compared to 6 percent). However, our results do not support that the impact of energy performance certificates varies in the price distribution. Consequently, the certificates are not differently capitalized in the high-end housing price segment. Finally, our results support the hypothesis that the energy performance certificate should be more capitalized into house prices in the northern and colder parts of Sweden than in the southern regions.
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Guadalajara, Natividad, María-Teresa Caballer, and Jose M. Osca. "Assessing localization impact on land values: a spatial hedonic study." Spanish Journal of Agricultural Research 17, no. 3 (November 8, 2019): e0110. http://dx.doi.org/10.5424/sjar/2019173-14961.
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Aim of study: To obtain spatial land valuing models using Geographic Information Systems (GIS), which collect spatial autocorrelation and improve the conventional models estimated by OLS (Ordinary Least Squares) to determine and quantify the factors explaining these values.Area of study: The Spanish Autonomous Community of Aragón, Spain.Material and methods: The mean land values per municipality and the land uses published by the Aragonese Statistics Institute were used, as well as the geographic, agricultural, demographic, economic and orographic characteristics of these municipalities. The Spatial Lag Model and the Spatial Error Model were compared with OLS in general terms and for uses.Main results: The statistics (R2, log likelihood, Akaike’s information criterion, Schwarz’s criterion) demonstrated that spatial models always outperformed conventional models. The tests based on the Lagrange Multiplier and Likelihood Ratio tests were significant at 99%. The importance of both agricultural and non-agricultural factors for determining the arable land value was confirmed. The land value increased with irrigation availability (by a mean of 2.2-fold for the set of all land uses), plot size (by 5.7% for each 1 ha increase), population size, income and location in nature reserves (11.02-12.89%).Research highlights: Results indicate the need to develop spatial models when modeling land prices by implementing GIS.
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Xia, Zelong, Hao Li, and Yuehong Chen. "Assessing Neighborhood Walkability Based on Usage Characteristics of Amenities under Chinese Metropolises Context." Sustainability 10, no. 11 (October 25, 2018): 3879. http://dx.doi.org/10.3390/su10113879.
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In the age of rapid motorization, walking, as both a green travelling mode and the most common form of daily physical activity, has been given increasing attention in Chinese metropolises. Walkability describes the extent to which a neighborhood environment is walking-friendly and recently has been regarded as a potential impetus for inflated housing prices. In this paper, we develop a walkability measurement model considering residents’ usage characteristics of the amenity, which incorporates three key factors: (1) amenity usage frequency; (2) amenity selection diversity; (3) the distance decay effect. Accordingly, we employ the proposed method to the case of Nanjing City and identify a clear spatial pattern of spatial heterogeneity in walkability among the 4143 dwelling areas within it. The experimental results suggest that the distribution of the residential walkability score varied greatly within Nanjing. It can be seen that dwelling areas with a high walkability score were clustered in the urban central regions and most dwelling areas showed a low walkability. Then, we utilized the hedonic price model to explore the correlation between neighborhood walkability and housing prices. The results show that the effects of community walkability on housing prices were statistically significant in Nanjing. Thus, we can infer that high walkability communities generally have concentrated rich amenity resources, and consequently have high property values.
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Cellmer, Radoslaw, and Radoslaw Trojanek. "Towards Increasing Residential Market Transparency: Mapping Local Housing Prices and Dynamics." ISPRS International Journal of Geo-Information 9, no. 1 (December 18, 2019): 2. http://dx.doi.org/10.3390/ijgi9010002.
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This article attempts to use spatial maps as a way of presenting additional information about the phenomena occurring in the housing market. In our opinion, spatial maps may facilitate understanding and provide more detailed information, which undoubtedly should increase the transparency of the housing market. The study used 12,219 transactions of apartments in Poznań in the years 2013–2017. General principles of price visualization activity and housing market dynamics were established in this study. The map of prices may reflect the location values determined by the quality of the urban infrastructure, distance from specific locations, and environmental factors. Market activity maps reveal areas where the market is dynamically developing, while information on trends in the number of transactions and price changes may demonstrate the growing or declining attractiveness of areas. The research is based on a model of hedonic regression in the form of ordinary least squares (OLS), quantile regression (QR), and geographically weighted regression (GWR). The maps presented should increase the transparency of the residential market (e.g., by providing more detailed information). However, one should bear in mind the limitations in the use of these methods resulting from a small number of transactions in a thin market.
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Cho, Eum Jang, and Jung Min Jang. "A Study on the Effect of Development Projects in Pyeongtaek City on Housing Price by Region." Korea Real Estate Academy 87 (August 31, 2022): 163–74. http://dx.doi.org/10.31303/krear.2022.87.163.
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(1) RESEARCH OBJECTIVES The purpose of this study is to study how the real estate development project of Pyeongtaek City affects housing prices by subdividing the northern, southern, and western regions by region.
(2) RESEARCH METHOD This study employed the Hedonic Price Model to achieve the major objectives of this study.
(3) RESEARCH FINDINGS The implications of this study are as follows. First, through this study, it was found that although the start of development projects raised the housing price, it did not falls within the spatial scope of this study, was judged to be meaningful to examine the differences by region as a place where the differences by region are clearly visible.
2. RESULTS The results of this study are as follows. The influence of other characteristics did not differ significantly by region, but in terms of development characteristics, it was found that the SRT Jije Station start and Samsung Electronics start construction increase the apartment transaction price in the southern and western regions except for the northern region.
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Usman, Hamza, Mohd Lizam, and Burhaida Burhan. "A Priori Spatial Segmentation of Commercial Property Market using Hedonic Price Modelling." Real Estate Management and Valuation 29, no. 2 (June 1, 2021): 16–28. http://dx.doi.org/10.2478/remav-2021-0010.
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Abstract The improvement of property price modelling accuracy using property market segmentation approaches is well documented in the housing market. However, that cannot be said of the commercial property market which is adjudged to be volatile, heterogeneous and thinly traded. This study, therefore, determines if the commercial property market in Malaysia is spatially segmented into submarkets and whether accounting for the submarkets improves the accuracy of price modelling. Using a 11,460 shop-offices transaction dataset, the commercial property submarkets are delineated by using submarket binary dummies in the market-wide model and estimating a separate hedonic model for each submarket. The former method improves the model fit and reduces error by 5.6% and 6.5% respectively. The commercial property submarkets are better delineated by estimating a separate hedonic model for each submarket as it improves the model fit by about 7% and reduces models’ error by more than 10%. This study concludes that the Malaysian commercial property market is spatially segmented into submarkets. Modelling the submarkets improves the accuracy and correctness of price modelling.
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Głuszak, Michał, Jarosław Czerski, and Robert Zygmunt. "Estimating repeat sales residential price indices for Krakow." Oeconomia Copernicana 9, no. 1 (March 31, 2018): 55–69. http://dx.doi.org/10.24136/oc.2018.003.
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Research background: There are several methods to construct a price index for infrequently traded real estate assets (mainly residential, but also office and land). The main concern to construct a valid and unbiased price index is to address the problem of heterogeneity of real estate or put differently to control for both observable and unobservable quality attributes. The one most frequently used is probably the hedonic regression methodology (classic, but recently also spatial and quantile regression). An alternative approach to control for unobservable differences in assets’ quality is provided by repeat sales methodology, where price changes are tracked based on differences in prices of given asset sold twice (or multiple times) within the study period. The latter approach is applied in renown S&P CoreLogic Case-Shiller house price indices.
Purpose of the article: The goal of the paper is to assess the applicability of repeat sales methodology for a major housing market in Poland. Previous studies used the hedonic methodology or mix adjustment techniques, and applied for major metropolitan areas. The most widely known example is the set of quarterly house price indices constructed by NBP — especially for the primary and secondary market. The repeat sales methodology has not been adopted with significant success to date — mainly because of concern regarding relative infrequency of transactions on the housing market in most metropolitan areas (thus a potentially small sample of repeated sales).
Methods: The study uses data on repeat sales of residential transactions in Krakow from 2003 to 2015. We apply different specifications of repeat sales index construction and compare respective values to the hedonic price index for Krakow estimated by NBP.
Findings & Value added: Findings suggest that repeat sales house sales indices can be used to track price dynamics for major metropolitan areas in Poland. The study suggests problems that need to be addressed in order to get unbiased results — mainly data collection mechanism and estimation procedure.
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Hu, Tao, and Haoyu Song. "Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective." Sustainability 15, no. 1 (December 29, 2022): 617. http://dx.doi.org/10.3390/su15010617.
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The goal of investors in the hotel business is to maximize profits, and the price is an important means of achieving this goal. This has attracted many scholars to study the spatiotemporal relationship between hotel room prices and their possible influencing factors from different perspectives. However, most existing studies adopt the linear assumption of the hedonic model, with limited features and a lack of feature selection procedures. Additionally, there are few forecasts of hotel pricing from a spatial perspective. To overcome these gaps, this study adopts linear and nonlinear machine learning methods based on the “big data” of Sanya City to explore the influencing factors of budget hotel pricing. Based on the spatial perspective, 81 potential factors were considered. They are further selected using a feature extraction model called recursive feature elimination. Six machine-learning algorithms were evaluated and compared: random forest, extreme gradient boosting, multi-linear regression, support vector regression, multilayer perceptron regression, and K-nearest neighbor regression. The optimal value was used to further calculate the feature importance. They disclosed 40 important impact characteristics and predicted the spatial distribution of hotel pricing.
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Xi, Hao, Lin Tang, and Changchun Feng. "Research on the Measurement Method of Benchmark Price of Rental Housing." Land 11, no. 5 (May 22, 2022): 759. http://dx.doi.org/10.3390/land11050759.
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China’s rental housing market has just started to develop in recent years. It is relatively imperfect and lacks a clear reference for the pricing of rents, which is not fully transparent. A study on the rent formation mechanism of rental housing has policy implications for the construction of a guiding price for the rental housing market and the establishment of a reference basis for the pricing of subsidized housing. Referring to the definition of a benchmark land price, we use data from Beijing to innovatively introduce the concept of benchmark rent. Based on hedonic price theory and the driving factors of benchmark rent, a system of indicators is constructed to explore the mechanism of influencing factors at meso and micro levels on the benchmark rent of market-based rental housing. After LaGrange and robustness tests, it is found that the spatial error model (SEM) is more suitable for benchmark rent determination. We conclude that benchmark rents are affected by spatial relationships caused by spatial heterogeneity and dependency, and that there is significant spatial variation in the factors affecting market-based rental housing benchmark rents. The determination of the benchmark rent can be used as a guiding signal for the market, as a clear signal expectation for the market, government, and tenants.
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Herath, Shanaka. "Elevating the Value of Urban Location: A Consumer Preference-Based Approach to Valuing Local Amenity Provision." Land 10, no. 11 (November 11, 2021): 1226. http://dx.doi.org/10.3390/land10111226.
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Estimating the non-market monetary values of urban amenities has become commonplace in urban planning research, particularly following Rosen’s seminal article on hedonic theory in 1974. As a revealed preference method, the hedonic approach decouples the market price of a house into price components that are attributable to housing characteristics. Despite the potential contribution of this theory in a planning context, three main limitations exist in the conventional applications: (1) variable measurement issues, (2) model misspecification, and (3) the problematic common use of global regression. These flaws problematically skew our understanding of the urban structure and spatial distribution of amenities, leading to misinformed policy interventions and poor amenity planning decisions. In this article, we propose a coherent conceptual framework that addresses measurement, specification, and scale challenges to generate consistent economic estimates of local amenities. Finally, we argue that, by paying greater attention to the spatial equity of amenity values, governments can provide greater equality of opportunities in cities.
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Kunimi, Takara, and Hajime Seya. "Identification of the geographical extent of an area benefiting from a transportation project: A generalized synthetic control." Journal of Transport and Land Use 14, no. 1 (January 5, 2021): 25–45. http://dx.doi.org/10.5198/jtlu.2021.1784.
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In evaluating the benefits of an infrastructure project, it is essential to consider who is benefiting from the project and where benefits are located. However, there is no established way to accurately determine the latter. To fill this methodological gap, this study proposes an approach for the ex-post identification of the geographical extent of an area benefiting from a transportation project based on a generalized synthetic control method. Specifically, it allows comparing multiple treatment units with their counterfactuals in a single run—changes in land prices (actual outcome) at each treated site are compared to the counterfactual outcome, and the individual (i.e., unit-level) treatment effect on the treated site is then estimated. This approach is empirically applied to a large-scale Japanese heavy railway, the Tsukuba Express line project. Our approach enables the detection of 1) the complicated spatial shape of benefit incidence; 2) negative spillovers; and 3) the increase in options (train routes), typically not considered in a benefit evaluation system based on the hedonic approach, but which can be capitalized into land prices.
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Benedetti, Ilaria, Luigi Biggeri, and Tiziana Laureti. "Sub-National Spatial Price Indexes for Housing: Methodological Issues and Computation for Italy." Journal of Official Statistics 38, no. 1 (March 1, 2022): 57–82. http://dx.doi.org/10.2478/jos-2022-0004.
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Abstract It is essential to measure within-country differences in housing costs in order to evaluate costs of living, assessing and comparing poverty levels, quantifying salaries and disposable income of families and finally for designing housing policies at local level. To the authors knowledge, no studies have yet been carried out on the computation of Space Price Indexes for Housing Rents (SPIHRs). In this article we computed preliminary estimates of sub-national SPIHRs by using hedonic regression model, which is an extension of the Country Product Dummy method, for all the Italian regions. The hedonic regression is generally used to obtain multilateral spatial indexes, thus allowing us to obtain multilateral SPIHRs for the Italian regions. The estimates have been done using 2017 data from the Real Estate Market Observatory which is a part of the Italian Agency of Revenue and Tax. This data source is the most comprehensive source of information on Italian houses price rents with a wide geographical coverage, including data for each Italian municipality. The obtained results show significant differences across the Italian regions, thus highlighting the importance of calculating SPIHR in Italy on a regular basis and the need to continue researches in this field.
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MIHAESCU, OANA, and RAINER VOM HOFE. "USING SPATIAL REGRESSION TO ESTIMATE PROPERTY TAX DISCOUNTS FROM PROXIMITY TO BROWNFIELDS: A TOOL FOR LOCAL POLICY-MAKING." Journal of Environmental Assessment Policy and Management 15, no. 01 (March 2013): 1350008. http://dx.doi.org/10.1142/s1464333213500087.
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This paper assesses the discount in property values due to proximity to brownfields using a spatial hedonic price model. Using two Bayesian hedonic pricing models, namely the spatial lag of X (SLX) model and the spatial Durbin error model (SDEM), this study identifies a significant decrease in property values for properties located within 2,000 feet of a brownfield. The loss in property value and the subsequent decrease in tax revenue for the City of Cincinnati, Ohio, are then calculated based on these results. Using logarithmic transformations of the property value and the distance to the nearest brownfield variables, we calculate that a 1% increase in the average distance to the closest brownfield leads to a 0.0893% increase in market value. This translates into a $2,262,569 total annual revenue loss for the City of Cincinnati that could presumably be recovered following brownfield cleanup. In addition to accounting for the phenomenon of spatial dependence, this study contributes to the urban planning and environmental policy literature by providing a method for local policy-makers to identify and estimate the negative effects of brownfield sites on local tax revenue.
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Keskin, Berna, Richard Dunning, and Craig Watkins. "Modelling the impact of earthquake activity on real estate values: a multi-level approach." Journal of European Real Estate Research 10, no. 1 (May 2, 2017): 73–90. http://dx.doi.org/10.1108/jerer-03-2016-0014.
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Purpose This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market. Design/methodology/approach The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region. Findings The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values. Research limitations/implications The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets. Practical implications The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments. Social implications The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk. Originality/value The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.
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Sylla, Marta, Tadeusz Lasota, and Szymon Szewrański. "Valuing Environmental Amenities in Peri-Urban Areas: Evidence from Poland." Sustainability 11, no. 3 (January 22, 2019): 570. http://dx.doi.org/10.3390/su11030570.
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Ecosystems provide services that contribute to the well-being of people living within a city’s borders and on the urban–rural fringe. While benefits from green areas in urban settings are well investigated, peri-urban areas are significantly less addressed by researchers than cities. This study aims to evaluate the importance of environmental amenities in peri-urban areas using the hedonic pricing method to assess people’s willingness to pay for environmental goods and services. A local regression model (geographically weighted regression) and two global regression models (generalized spatial two-stage least squares and ordinary least square) are used to identify the spatial patterns and level of influence of protected areas, forests, rivers, trees, and landscape diversity. This paper includes the Central European case study example of a peri-urban area of the city of Wroclaw, Poland. The results from the three models show that out of all of the environmental amenities included in this study, proximity to protected areas—such as Natura 2000 sites and landscape parks—and the diversity of land-use patches within the 500-m radius around the sites exert the strongest influence on plot prices. The overall impact of environmental amenities on vacant plot prices in peri-urban areas is low or, as in the case of river and streams, not significant. The results of the analysis reveal the preferences of the new peri-urban inhabitants concerning green spaces that have an effect on the real estate market in Poland.
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Dell’Anna, Bravi, Marmolejo-Duarte, Bottero, and Chen. "EPC Green Premium in Two Different European Climate Zones: A Comparative Study between Barcelona and Turin." Sustainability 11, no. 20 (October 11, 2019): 5605. http://dx.doi.org/10.3390/su11205605.
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Energy performance certificates (EPCs) are important tools aimed at improving buildings’ energy performance. They play a central role in the context of the Energy Performance of Buildings Directive (EPBD) which asks member states (MS) to take the necessary measures to establish a complete certification system. In this study, an application of the hedonic price method (HPM) assessing the effect of energy labels derived from the EPC on real estate market value is presented. The estimation methodology was applied to two European cities characterized by different climate conditions. The analysis was based on two datasets of listing prices referring to multi-family residential markets in Turin (Italy) and Barcelona (Spain). Four models for each dataset were applied to capture the marginal price of green attributes, but also to control for the spatial autocorrelation among values. The findings showed how the EPC has been applied in the two countries and how it has influenced the real estate market. Turin’s buyers pay more attention to the EPC label, while in Barcelona, they value much more single characteristics, such as air conditioning and a swimming pool, considered popular attributes among contemporary buildings in this climate zone. From the results, it is possible to deduce that the implementation of the EPC schemes is still irregular in EU countries and must be strengthened through a standardized rating model.
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Buda, Erez, Dani Broitman, and Daniel Czamanski. "Urban Structure in Troubled Times: The Evolution of Principal and Secondary Core/Periphery Gaps through the Prism of Residential Land Values." Sustainability 13, no. 10 (May 20, 2021): 5722. http://dx.doi.org/10.3390/su13105722.
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The structure of modern cities is characterized by the uneven spatial distribution of people and activities. Contrary to economic theory, it is neither evenly distributed nor entirely monocentric. The observed reality is the result of various feedbacks in the context of the interactions of attraction and repulsion. Heretofore, there is no agreement concerning the means to measuring the dimensions of these interactions, nor the framework for explaining them. We propose a simple model and an associated method for testing the interactions using residential land values. We claim that land values reflect the attractiveness of each location, including its observable and unobservable characteristics. We extract land values from prices of residences by applying a dedicated hedonic model to extensive residential real estate transaction data at a detailed spatial level. The resulting land values reflect the attractiveness of each urban location and are an ideal candidate to measure the degree of centrality or peripherality of each location. Moreover, assessment of land values over time indicates ongoing centralization and peripheralization processes. Using the urban structure of a small and highly urbanized country as a test case, this paper illustrates how the dynamics of the gap between central and peripheral urban areas can be assessed.
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Dubé, Jean, Maha AbdelHalim, and Nicolas Devaux. "Evaluating the Impact of Floods on Housing Price Using a Spatial Matching Difference-In-Differences (SM-DID) Approach." Sustainability 13, no. 2 (January 15, 2021): 804. http://dx.doi.org/10.3390/su13020804.
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Many applications have relied on the hedonic pricing model (HPM) to measure the willingness-to-pay (WTP) for urban externalities and natural disasters. The classic HPM regresses housing price on a complete list of attributes/characteristics that include spatial or environmental amenities (or disamenities), such as floods, to retrieve the gradients of the market (marginal) WTP for such externalities. The aim of this paper is to propose an innovative methodological framework that extends the causal relations based on a spatial matching difference-in-differences (SM-DID) estimator, and which attempts to calculate the difference between sale price for similar goods within “treated” and “control” groups. To demonstrate the potential of the proposed spatial matching method, the researchers present an empirical investigation based on the case of a flood event recorded in the city of Laval (Québec, Canada) in 1998, using information on transactions occurring between 1995 and 2001. The research results show that the impact of flooding brings a negative premium on the housing price of about 20,000$ Canadian (CAN).
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Wei, Cankun, Meichen Fu, Li Wang, Hanbing Yang, Feng Tang, and Yuqing Xiong. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data." Land 11, no. 3 (February 24, 2022): 334. http://dx.doi.org/10.3390/land11030334.
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In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model.
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Ismail, Mohammad, Abukar Warsame, and Mats Wilhelmsson. "Do segregated housing markets have a spillover effect on housing prices in nearby residential areas?" Journal of European Real Estate Research 14, no. 2 (May 27, 2021): 169–86. http://dx.doi.org/10.1108/jerer-06-2020-0037.
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Purpose The purpose of this study is to analyse the trends regarding housing segregation over the past 10–20 years and determine whether housing segregation has a spillover effect on neighbouring housing areas. Namely, the authors set out to determine whether proximity to a specific type of segregated housing market has a negative impact on nearby housing markets while proximity to another type of segregated market has a positive impact. Design/methodology/approach For the purposes of this paper, the authors must combine information on segregation within a city with information on property values in the city. The authors have, therefore, used data on the income of the population and data on housing values taken from housing transactions. The case study used is the city of Stockholm, the capital of Sweden. The empirical analysis will be the estimation of the traditional hedonic pricing model. It will be estimated for the condominium market. Findings The results indicate that segregation, when measured as income sorting, has increased over time in some of the housing markets. Its effects on housing values in neighbouring housing areas are significant and statistically significant. Research limitations/implications A better understanding of the different potential spillover effects on housing prices in relation to the spatial distribution of various income groups would be beneficial in determining appropriate property assessment levels. In other words, awareness of this spillover effect could improve existing property assessment methods and provide local governments with extra information to make an informed decision on policies and services needed in different neighbourhoods. Practical implications On housing prices emanating from proximity to segregated areas with high income differs from segregated areas with low income, policies that address socio-economic costs and benefits, as well as property assessment levels, should reflect this pronounced difference. On the property level, positive spillover on housing prices near high-income segregated areas will cause an increase in the number of higher income groups and exacerbate segregation based on income. Contrarily, negative spillover on housing prices near low-income areas might discourage high-income households from moving to a location near low-income segregated areas. Local government should be aware of these spillover effects on housing prices to ensure that policies intended to reduce socioeconomic segregation, such as residential and income segregation, produce desirable results. Social implications Furthermore, a good estimation of these spillover effects on housing prices would allow local governments to carry out a cost–benefit analysis for policies intended to combat segregation and invest in deprived communities. Originality/value The main contribution of this paper is to go beyond the traditional studies of segregation that mainly emphasise residential segregation based on income levels, i.e. low-income or high-income households. The authors have analysed the spillover effect of proximity to hot spots (high income) and cold spots (low income) on the housing values of nearby condominiums or single-family homes within segregated areas in Stockholm Municipality in 2013.
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Liao, Wen-Chi, and Xizhu Wang. "Hedonic house prices and spatial quantile regression." Journal of Housing Economics 21, no. 1 (March 2012): 16–27. http://dx.doi.org/10.1016/j.jhe.2011.11.001.
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Gu, Guangtong. "Hedonic Price Ripple Effect and Consumer Choice: Evidence from New Homes." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 6 (October 20, 2018): 809–16. http://dx.doi.org/10.20965/jaciii.2018.p0809.
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This study uses the hedonic price model to examine the determinants of house prices. It employs kernel density to estimate the spatial weight matrix and conducts spatial econometrics and instrumental variables quantile regression analysis. Taking a new building in Shanghai city as an example, this micro-perspective study shows that hedonic prices of houses are derived from consumer hedonic preferences and their changes in terms of inertia and spatial ripple effects. However, there are large differences in the drivers of the same hedonic attribute across quantile degrees. With house prices gradually decreasing from the city center to the surrounding areas, the ripple effect is significant in determining residents’ preferred consumption characteristics and presents several inverted U-shaped and inverted U-shaped relationships. Residents’ preference for housing consumption is mainly reflected in the average area of the house, property fee, location of the administrative area, and so on. Regional real estate price changes are mainly reflected in hedonic house prices, and real consumer demand for housing is mainly derived from hedonic preference. Therefore, government regulation and control of house prices should consider different regions and different consumer groups simultaneously.
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Waights, Sevrin. "Does the law of one price hold for hedonic prices?" Urban Studies 55, no. 15 (January 31, 2018): 3299–317. http://dx.doi.org/10.1177/0042098017749403.
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Hedonic prices of locational attributes in urban land markets are determined by a process of spatial arbitrage that is similar to that which underpins the law of one price. If hedonic prices deviate from their spatial equilibrium values then individuals can benefit from changing locations. I examine whether the law holds for the hedonic price of rail access using a unique historical dataset for Berlin over the period 1890–1914, characterised by massive investment in the transport infrastructure. I estimate the hedonic price of rail access across multiple urban neighbourhoods and time periods to generate a panel dataset of hedonic price differences that I test for stationarity using a panel unit root test. Across multiple specifications I consistently fail to reject the null hypothesis of no unit root and accept the alternative hypothesis that the law holds. My estimates indicate a half-life for convergence to the law of one price that lies between 0.28 and 1.14 years. This result is consistent with spatial equilibrium.
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Huang, Zezhou, Ruishan Chen, Di Xu, and Wei Zhou. "Spatial and hedonic analysis of housing prices in Shanghai." Habitat International 67 (September 2017): 69–78. http://dx.doi.org/10.1016/j.habitatint.2017.07.002.
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Lisi, Gaetano. "Income capitalisation method and hedonic model: an integrated approach." Journal of Property Investment & Finance 37, no. 3 (April 10, 2019): 289–300. http://dx.doi.org/10.1108/jpif-11-2018-0088.
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Purpose The purpose of this paper is to provide an integrated approach that combines the two methods usually used in the real estate appraisals, namely, the income capitalisation method and the hedonic model. Design/methodology/approach In order to pull out the link between the income capitalisation approach and the hedonic model, the standard hedonic price function is introduced into the basic model of income capitalisation instead of the house market value. It follows that, from the partial derivative, a direct relation between hedonic prices and discount rate can be obtained. Finally, by using the close relationship between income capitalisation and direct capitalisation, a mathematical relation between hedonic prices and capitalisation rate is also obtained. Findings The developed method allows to estimate the capitalisation rate using only hedonic prices. Indeed, selling and hedonic prices incorporate all of the information required to correctly estimate the capitalisation rate. Furthermore, given the close relation among going-in and going-out capitalisation rates and discount rate, the proposed method could also be useful for determining both the going-out capitalisation rate and the discount rate. Practical implications Obviously, it is always preferable to estimate the capitalisation rate by just using comparable transactional data. Nevertheless, the method developed in this paper is especially useful when: the rental income data are missing and/or not entirely reliable; the data on rental income and house price are related to different homes; the capitalisation rate, in fact, should compare the rent and value of identical homes. In these cases, therefore, the method can be a valuable alternative to direct estimation. Originality/value The large and important literature on real estate economics and real estate appraisal neglects the relationship between hedonic prices and capitalisation rate, thus considering the hedonic model and the income capitalisation approach as two separate and alternative methods. This paper, instead, shows that integration is possible and relatively simple.
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Zhao, Chunfang, Yingliang Wu, Yunfeng Chen, and Guohua Chen. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China." Sustainability 15, no. 2 (January 16, 2023): 1703. http://dx.doi.org/10.3390/su15021703.
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An investigation into the pricing mechanism of Airbnb is crucial for achieving the sustainable development of sharing economy accommodations and has great academic and practical significance. The existing pricing studies on sharing economy accommodation have identified a set of important factors impacting prices based on the hedonic price model. However, the spatial scale of the impact of various hedonic attributes on Airbnb listing prices is not yet clear. This study takes Beijing, China, as a case study; develops a conceptual framework that incorporates four categories of hedonic attributes; and uses a spatial heterogeneity perspective to investigate the multiscale spatial effects of various attributes on the prices of Airbnb listings. Our findings revealed the following: (1) The explanatory power of different categories of attributes towards listing prices varies from high to low, as follows: functional attributes, locational attributes, reputational attributes, and host status attributes, among which the functional attributes are the most important determinants of Airbnb listing prices. (2) There are multiscale, spatially heterogeneous relationships between Airbnb listing attributes and prices. Specifically, the functional attribute variables have local influencing scales, the reputation attribute variables have regional scales, and the variables of host status and locational attributes have global scales. (3) Compared with ordinary least squares (OLS) regression and geographically weighted regression (GWR), multiscale geographic weighted regression (MGWR) improves overall modelling ability by introducing multiple scales and is better suited to illuminating the hedonic pricing of sharing economy accommodations. This study provides new insights into the spatially varied relationships between listing attributes and Airbnb listing prices, which can deepen our understanding of sharing economy accommodation and help hosts formulate location-based pricing strategies.
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Steimetz, Seiji S. C. "SPATIAL MULTIPLIERS IN HEDONIC ANALYSIS: A COMMENT ON “SPATIAL HEDONIC MODELS OF AIRPORT NOISE, PROXIMITY, AND HOUSING PRICES”*." Journal of Regional Science 50, no. 5 (December 2010): 995–98. http://dx.doi.org/10.1111/j.1467-9787.2010.00679.x.
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Kostov, Philip. "A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices." Spatial Economic Analysis 4, no. 1 (March 1, 2009): 53–72. http://dx.doi.org/10.1080/17421770802625957.
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Cohen, Jeffrey P., and Cletus C. Coughlin. "SPATIAL HEDONIC MODELS OF AIRPORT NOISE, PROXIMITY, AND HOUSING PRICES*." Journal of Regional Science 48, no. 5 (December 2008): 859–78. http://dx.doi.org/10.1111/j.1467-9787.2008.00569.x.
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Cohen, Jeffrey P., and Cletus C. Coughlin. "RESPONSE TO “SPATIAL MULTIPLIERS IN HEDONIC ANALYSIS: A COMMENT ON ‘SPATIAL HEDONIC MODELS OF AIRPORT NOISE, PROXIMITY, AND HOUSING PRICES’ ”." Journal of Regional Science 50, no. 5 (December 2010): 999–1000. http://dx.doi.org/10.1111/j.1467-9787.2010.00680.x.
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CHEN, Jieh-Haur, Chuan Fan ONG, Linzi ZHENG, and Shu-Chien HSU. "FORECASTING SPATIAL DYNAMICS OF THE HOUSING MARKET USING SUPPORT VECTOR MACHINE." International Journal of Strategic Property Management 21, no. 3 (July 11, 2017): 273–83. http://dx.doi.org/10.3846/1648715x.2016.1259190.
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This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.
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Chen, Yujiao, and Zhengbo Luo. "Hedonic Pricing of Houses in Megacities Pre- and Post-COVID-19: A Case Study of Shanghai, China." Sustainability 14, no. 17 (September 3, 2022): 11021. http://dx.doi.org/10.3390/su141711021.
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Housing price is one of the most concerning issues to the public worldwide. Studying the spatial characteristics of Shanghai’s housing prices and their explanatory factors is of great practical significance, for Shanghai is the largest city in China and serves as the national economic center and a global financial hub. By crawling the point of interest (POI) data from the Lianjia Real Estate and Gaode Map in the past decade and applying the multiscale geographically-weighted regression (MGWR) model, this study deeply explores the spatial characteristics of housing prices and their main influencing variables in Shanghai before and after the COVID-19 pandemic. Results show that housing prices in Shanghai kept rising even under the shock of the pandemic, especially in high-priced housing. After the pandemic, Shanghai’s housing price market polarization intensified. In addition, housing prices are very sensitive to location and have strong spatial heterogeneity. The influencing effects of different explanatory factors vary perceptibly in spatial heterogeneity as well as pre- and post- COVID-19.
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Panasolo, Alessandro, Franklin Galvão, Hermes Yukio Higachi, Edilson Batista de Oliveira, Fernando Campos de Oliveira, Carlos Augusto Wroblewski, Tatiana Maria Cecy Gadda, and Camila Fossa Balbinot. "Urban green areas and real estate prices in Curitiba, Brazil." Revista Ibero-Americana de Ciências Ambientais 11, no. 6 (July 6, 2020): 86–102. http://dx.doi.org/10.6008/cbpc2179-6858.2020.006.0008.
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We attempted to identify to which extent the implicit ecosystem service values of urban green areas impact real estate values in the city of Curitiba, Brazil. The study is based on spatial econometrics techniques and hedonic price theory applied to 43 urban green areas, highlighting three units: the Airumã Private Natural Heritage Reserve, the Teresa Urban Ecological Station, and the President Getulio Vargas Refinery. Information was obtained on the structural characteristics of more than 5,300 apartments and houses. The results of exploratory spatial data analysis (ESDA) and estimates from hedonic regression model parameters show that the presence of urban green areas contribute to the final property prices. The effects of proximity to urban green areas on the price of urban residential property are not homogeneous and stationary throughout urban spaces and can generate distinct spatial clusters of real estate prices: high-high and low-low. The used methodology proved to be efficient to assess the value of urban green areas with regard to the use of information, processing, data analysis, and results generated. Furthermore, it measured the impact of these areas on property prices and provided easily interpretable data that can be relevant for payments for ecosystem services policies at the local level.
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Zhang, Min, Lu Wang, Wanying Yang, Yiwei Li, and Yin Bai. "Host facial features and Airbnb listing prices: a spatial hedonic pricing approach." Journal of Travel & Tourism Marketing 38, no. 9 (November 22, 2021): 991–1003. http://dx.doi.org/10.1080/10548408.2021.2006861.
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