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Spatial Market Inefficiency in Housing Market: A Spatial Quantile Regression Approach

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Abstract

This paper empirically tests housing market efficiency in the spatial dimension by using the spatial autoregressive conditional heteroskedastic (ARCH) and spatial quantile regression models. The tests were conducted in terms of both housing returns and squared returns (volatility). The sale price data used is from Cook County residential MLS for the years 2010–2016. The main findings are that housing returns are not spatially correlated but squared returns are spatially correlated, and the spatial dependence of squared returns seems to be stronger for higher squared return quantiles.

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Notes

  1. Distance-based weight matrices (e.g., fixed distance band, inverse distance), have been also considered in empirical analysis for the robustness check in Section 6. The major conclusion still holds.

  2. One reviewer questioned the use of census tracts as a unit of analysis. Census tracts are small, relatively stable spatial units with population ranging between 2,500 and 8,000 with an average of approximately 4,000. Choice of appropriate spatial units in any empirical analysis of spatial data is a delicate issue. Depending on that choice the results will be different. For future researchers, depending on the availability of data, our suggestion would be to use spatial units of further disaggregated level, such as census block group (a potentially more homogeneous subdivision of the census tract).

  3. PUMAs are geographic areas defined for statistical use by the U.S. Census Bureau and constructed from census tracts.

  4. ArcGIS software was used to conduct this task.

  5. No additional components that are often included in computing the total return such as interest and dividends in the financial market data are considered in this analysis as they are not applicable in our context.

  6. Standard errors are calculated using a simple bootstrap estimator, see, Kim and Muller (2004). New samples are constructed by drawing with replacement from the rows of the data frame holding \(y\), \(Wy\), \(X\), and a set of instruments, \(Z\). The bootstrap standard errors are the standard deviations of the nboot re-calculations of the coefficient estimates (see the McSpatial package in R for further information). Given the dependent nature of our data, there are improved ways to conduct the bootstrap; for instance, see, Hounyo (2021).

  7. It is noteworthy that the coefficients in Table 6 do not directly reflect the marginal effects of the corresponding independent variables on the dependent variable (LeSage and Pace, 2010), we thus need to report the direct, indirect, and total effects of the independent variables. However, the focus of this study is on the spatial dependence parameter and not on the marginal effects of the independent variables.

  8. Henricks et al. (2018)

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Authors and Affiliations

  1. Federal Reserve Bank of Richmond, Richmond, VA, USA

    Jiyoung Chae

  2. Department of Economics, University of Illinois at Urbana-Champaign, Champaign, IL, USA

    Anil K. Bera

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  1. Jiyoung Chae
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Chae, J., Bera, A.K. Spatial Market Inefficiency in Housing Market: A Spatial Quantile Regression Approach. J Real Estate Finan Econ (2022). https://doi.org/10.1007/s11146-022-09923-y

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