An Analysis of Spatial Dependence in Real Estate Prices
- PDF / 847,363 Bytes
- 23 Pages / 439.642 x 666.49 pts Page_size
- 46 Downloads / 220 Views
An Analysis of Spatial Dependence in Real Estate Prices Orc¸un Moralı1 · Neslihan Yılmaz1 Accepted: 15 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Real estate properties are naturally location-fixed. When space related factors are not fully incorporated in a standard pricing equation, spatial autocorrelation is likely to exist. This results in inefficiencies in estimations and raises the need for more complex spatial models. This paper analyzes the determinants of spatial dependence and evaluates the performance of the hedonic regression equation when the determinants of spatial dependence are controlled for. Using a novel dataset for a metropolitan housing market, we document the spatial clustering of housing characteristics such as area, total number of floors and the building age. We find support for the hypotheses that the construction process, shared social services and high-rise residential complexes cause spatial correlation. Our findings show that spatial correlation is significantly reduced when the factors of spatial dependence and district level data is controlled for in the standard hedonic regression. Keywords Spatial analysis · Spatial autocorrelation · Spatial dependence · House pricing · Emerging markets
Introduction The models for pricing real estate properties involve a variety of factors including spatial characteristics. Space is unique to a property and each single property has its own pricing dynamics. Location is one factor, though, space does not always influence a property uniquely. Spatial effects can be property-specific such as a particular view; area-specific like the proximity to public transportation; neighborhood-specific such as the average income level of the residents; or more general such as a major investment plan for the city.
Neslihan Yılmaz
[email protected] 1
Department of Management, Boˇgazic¸i University, Istanbul, Turkey
O. Moralı, N. Yılmaz
Spatial factors result in spatial dependence of properties, thus, clustering in property prices across space. When spatial factors are not fully incorporated, spatial autocorrelation in a standard hedonic regression’s residuals is likely to exist. To overcome the problem of inefficient estimators at the presence of spatial autocorrelation, several econometric models are introduced over the last few decades. While using such models facilitates a better use of the data, it complicates the model and economic interpretation of spatial factors. Therefore, identifying the factors of spatial dependence is of interest; moreover, separating out the locational effects lays the groundwork for quantifying and predicting the effects of particular locational factors on real estate prices. In this study, we analyze the performance of the hedonic regression equation while controlling for the determinants of spatial dependence and examine the causes of spatial dependence using a novel data set for the metropolitan city of Istanbul, Turkey. The cross-sectional data we use consist of 2
Data Loading...
