EXAMINING SPATIAL AUTOCORRELATION OF REAL ESTATE FEATURES USING MORAN STATISTICS

Purpose Information transparency is crucially important in price setting in real estate, particularly when information asymmetry is concerned. This paper aims to examine how a change in government policy in relation to information disclosure and transparency impacts residential real estate price discovery. Specially, this paper investigates how real estate traders determined asking prices in the context of the Scottish housing market before and after the implementation of the Home Report, which aimed to prevent artificially low asking prices. Design/methodology/approach This paper uses spatial lag hedonic pricing models to empirically observe how residential asking prices are determined by property sellers in response to a change in government policy that is designed to enhance market transparency. It uses over 79,000 transaction data of the Aberdeen residential market for the period of Q2 1998 to Q2 2013 to test the models. Findings The empirical findings provide some novel insights in relation to the price determination within the residential market in Scotland. The spatial lag models suggest that spatial autocorrelation in property prices has increased since the Home Report came into effect, indicating that property sellers have become more prone to infer asking prices based on prior sales of dwellings in close vicinity. The once-common practice of setting artificially low asking prices seems to have dwindled to a certain extent statistically. Originality/value The importance of understanding the relationship between information transparency and property price determination has gathered momentum over the past decade. Although spatial hedonic techniques have been extensively used to study the impact of various property- and neighbourhood-specific attributes on residential real estate market in general, surprisingly little is known about the empirical relationship between spatial autocorrelation in real estate prices and information transparency.

Download Full-text

GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China

Complexity ◽

10.1155/2020/1079024 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Shaopei Chen ◽

Dachang Zhuang ◽

Huixia Zhang

Keyword(s):

Real Estate ◽

Spatial Autocorrelation ◽

Large Scale ◽

Housing Prices ◽

Geographical Location ◽

Housing Price ◽

Housing Markets ◽

Linear Regression Models ◽

Service Intensity ◽

Spatial Homogeneity

In the past decades, the booming growth of housing markets in China triggers the urgent need to explore how the rapid urban spatial expansion, large-scale urban infrastructural development, and fast-changing urban planning determine the housing price changes and spatial differentiation. It is of great significance to promote the existing governing policy and mechanism of housing market and the reform of real-estate system. At the level of city, an empirical analysis is implemented with the traditional econometric models of regressive analysis and GIS-based spatial autocorrelation models, focusing in examining and characterizing the spatial homogeneity and nonstationarity of housing prices in Guangzhou, China. There are 141 neigborhoods in Guangzhou identified as the independent individuals (named as area units), and their values of the average annual housing prices (AAHP) in (2009–2015) are clarified as the dependent variables in regressing analysis models used in this paper. Simultaneously, the factors including geographical location, transportation accessibility, commercial service intensity, and public service intensity are identified as independent variables in the context of urban development and planning. The integration and comparative analysis of multiple linear regression models, spatial autocorrelation models, and geographically weighted regressing (GWR) models are implemented, focusing on exploring the influencing factors of house prices, especially characterizing the spatial heterogeneity and nonstationarity of housing prices oriented towards the spatial differences of urban spatial development, infrastructure layout, land use, and planning. This has the potential to enrich the current approaches to the complex quantitative analysis modelling of housing prices. Particularly, it is favorable to examine and characterize what and how to determine the spatial homogeneity and nonstationarity of housing prices oriented towards a microscale geospatial perspective. Therefore, this study should be significant to drive essential changes to develop a more efficient, sustainable, and competitive real-estate system at the level of city, especially for the emerging and dynamic housing markets in the megacities in China.

Download Full-text

Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

SSRN Electronic Journal ◽

10.2139/ssrn.2055236 ◽

2012 ◽

Author(s):

Siu Kei Wong ◽

Chung Yim Edward Yiu ◽

K.W. Chau

Keyword(s):

Real Estate ◽

Spatial Autocorrelation ◽

Trading Volume ◽

Real Estate Prices

Download Full-text

International Real Estate Review

International Real Estate Review ◽

10.53383/100020 ◽

2000 ◽

Vol 3 (1) ◽

pp. 34-48

Author(s):

Charles C. Cartern ◽

◽

William J. Haloupek ◽

Keyword(s):

Real Estate ◽

Spatial Autocorrelation ◽

Housing Market ◽

Least Squares ◽

Site Selection ◽

Weighted Least Squares ◽

Empirical Studies ◽

Shopping Centers ◽

Single Family ◽

Family Home

This paper describes and applies the weighted least squares (WLS) technique that corrects for spatial autocorrelation in the residuals of hedonic regressions. Most empirical studies to date have focused on spatial autocorrelation in the housing market, i.e., single family home valuation. This study focuses on mall stores within shopping centers, with an emphasis on retail site selection within the mall.

Download Full-text