Analysis of Spatial Autocorrelation in House Prices

This article examines spatial autocorrelation in transaction prices of single-family properties in Dallas, Texas. The empirical analysis is conducted using a semilog hedonic house price equation and a spherical autocorrelation function with data for over 5000 transactions of homes sold between 1991:4 and 1993:1. Properties are geocoded and assigned to separate housing submarkets within metropolitan Dallas. Hedonic and spherical autocorrelation parameters are estimated separately for each submarket using estimated generalized least squares (EGLS). We find strong evidence of spatial autocorrelation in transaction prices within submarkets. Results for spatially autocorrelated residuals are mixed. In four of eight submarkets, there is evidence of spatial autocorrelation in the hedonic residuals for single-family properties located within a 1200 meter radius. In two submarkets, the hedonic residuals are spatially autocorrelated throughout the submarket, while the hedonic residuals are spatially uncorrelated in the remaining two submarkets. Finally, we compare OLS and kriged EGLS predicted values for properties sold during 1993:1. Kriged EGLS predictions are more accurate than OLS in six of eight submarkets, while OLS has smaller prediction errors in submarkets where the residuals are spatially uncorrelated and the estimated semivariogram has a large variance.     

A Spatial Autocorrelation Approach for Examining the Effects of Urban Greenspace on Residential Property Values

This paper presents spatially explicit analyses of the greenspace contribution to residential property values in a hedonic model. The paper utilizes data from the housing market near downtown Los Angeles. We first used a standard hedonic model to estimate greenspace effects. Because the residuals were spatially autocorrelated, we implemented a spatial lag model as indicated by specification tests. Our results show that neighborhood greenspace at the immediate vicinity of houses has a significant impact on house prices even after controlling for spatial autocorrelation. The different estimation results from non-spatial and spatial models provide useful bounds for the greenspace effect. Greening of inner city areas may provide a valuable policy instrument for elevating depressed housing markets in those areas.     

Spatial and Temporal Dependence in House Price Prediction

This paper incorporates spatial and temporal dependence among housing transactions in predicting future house prices. We employ the spatiotemporal autoregressive model and structure the spatial and temporal weighting matrices as in Pace et al. (1998). We control for the time variation of both the attribute prices and the spatial and temporal dependence parameters through performing the analysis on an annual basis. Spatial heterogeneity is accounted for using experience-based definition of submarkets by real estate professionals. Using a comprehensive housing transaction data set from the Dutch Randstad region, we show that integrating the spatial and temporal dependence within the hedonic modeling improves the prediction power as compared to traditional hedonic model that neglects these effects.     

Spatial Autocorrelations and Urban Housing Market Segmentation

This paper seeks to let data define urban housing market segments, replacing the conventional administrative or any pre-defined boundaries used in the previous housing submarket literature. We model housing transaction data using a conventional hedonic function. The hedonic residuals are used to estimate an isotropic semi-variogram, from which residual variance–covariance matrix is constructed. The correlations between hedonic residuals are used as identifier to assign housing units into clusters. Standard submarket identification tests are applied to each cluster to examine the segmentation of housing market. The results are compared with the prevailing structure of market segments. Weighted mean square test shows that the defined submarket structure can improve the precision of price prediction by 17.5%. This paper is experimental in the sense that it represents one of the first attempts at investigating market segmentation through house price spatial autocorrelations.     

The Predictive Power of Anisotropic Spatial Correlation Modeling in Housing Prices

This paper develops a method to capture anisotropic spatial autocorrelation in the context of the simultaneous autoregressive model. Standard isotropic models assume that spatial correlation is a homogeneous function of distance. This assumption, however, is oversimplified if spatial dependence changes with direction. We thus propose a local anisotropic approach based on non-linear scale-space image processing. We illustrate the methodology by using data on single-family house transactions in Lucas County, Ohio. The empirical results suggest that the anisotropic modeling technique can reduce both in-sample and out-of-sample forecast errors. Moreover, it can easily be applied to other spatial econometric functional and kernel forms.     

Spatial Variation in the Determinants of House Prices and Apartment Rents in China

This paper provides an examination of China??s residential real estate market at the county level using data from that country??s 2000 census. The market is a new one, having only been fully established in 1998. The analysis in the paper is in the form of an aggregate (county-level) hedonic model specified in two versions. Global parameters results are estimated using spatial error model specifications while more local effects are estimated by geographically weighted regression. Global results are typical in that structural characteristics such as floor space and contextual characteristics such as level of in-migration are important in residential prices. Local results, however, indicate significant spatial variation in the effect of both structural amenities and locational context on housing prices. In a simpler specification, rents are shown to respond positively to both median house prices levels and the supply of apartments available at market prices, but also with significant spatial variation across China.     

Residential Construction Costs and the Supply of New Housing: Endogeneity and Bias in Construction Cost Indexes

Change in the level of residential construction affects macroeconomic conditions and is an important determinant of movements in house prices. Theory teaches us that increases in the cost of construction should reduce the supply of new housing. Yet empirical research has failed to find a consistent relationship between these costs and housing starts. This article introduces an entirely new set of micro-data on housing construction costs to study this issue. We develop quality-controlled, hedonic construction cost series from these data. Using this series, we estimate housing supply and construction cost functions for new single-family residences. This research demonstrates that bias in the commercial cost indexes used in existing housing supply studies is a likely cause of their poor performance in existing estimates of the supply of new single-family housing. The bias appears to be caused by an incorrect measure of labor costs and a failure to address the endogeneity of construction costs and construction activity. In contrast, starts regressions using the hedonic cost series generate much more sensible results. We find that housing starts are quite cost elastic; construction costs are endogenous in the new housing supply function, and the cost shares of material and labor in the structure of new residences are approximately 65 and 35%, respectively.     

Spatial Dependence,Housing Submarkets,and House Price Prediction

This paper compares alternative methods of controlling for the spatial dependence of house prices in a mass appraisal context. Explicit modeling of the error structure is characterized as a relatively fluid approach to defining housing submarkets. This approach allows the relevant submarket to vary from house to house and for transactions involving other dwellings in each submarket to have varying impacts depending on distance. We conclude that—for our Auckland, New Zealand, data—the gains in accuracy from including submarket variables in an ordinary least squares specification are greater than any benefits from using geostatistical or lattice methods. This conclusion is of practical importance, as a hedonic model with submarket dummy variables is substantially easier to implement than spatial statistical methods.

Spatial Dependence and House Price Index Construction

Accurate estimation of prevailing metropolitan housing prices is important for both business and research investigations of housing and mortgage markets. This is typically done by constructing quality-adjusted house price indices from hedonic price regressions for given metropolitan areas. A major limitation of currently available indices is their insensitivity to the geographic location of dwellings within the metropolitan area. Indices are constructed based on models that do not incorporate the underlying spatial structure in housing data sets. In this article, we argue that spatial structure, especially spatial dependence latent in housing data sets, will affect the precision and accuracy of resulting price estimates. We illustrate the importance of spatial dependence in both the specification and estimation of hedonic price models. Assessments are made on the importance of spatial dependence both on parameter estimates and on the accuracy of resulting indices.     

住房财富对中国城镇家庭消费的影响

在构建以国内大循环为主体,国内国际双循环相互促进的新发展格局下,把握扩大内需这一战略基点,激发居民消费潜力,是推动经济高质量发展的关键之一。住房已经成为中国家庭财富的重要组成部分,一方面可通过财富效应促进家庭消费,另一方面也可能由于“房奴效应”降低家庭消费。因此,住房财富对家庭消费的影响方向并不确定。本文基于2013-2019年中国家庭金融调查数据,研究了住房财富对家庭消费的影响,并检验了住房财富影响家庭消费的可能渠道。研究发现,住房财富对城镇家庭消费有显著促进作用,并显著改善了家庭消费结构,住房资产具有财富效应。进一步研究发现,住房财富能够缓解流动性约束,从而提高家庭消费水平。异质性分析表明,住房财富对不同类型的消费具有不同的促进作用,不同地区和拥有住房数量的差别均会对住房财富产生不同影响。根据本文研究,在控制风险的前提下,可发挥既有住房财富对平滑家庭消费的积极作用,促进家庭消费增长,改善家庭消费结构,进一步推进家庭消费升级。