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Combining Property Price Predictions from Repeat Sales and Spatially Enhanced Hedonic Regressions

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Abstract

Hedonic regression and repeat sales are commonly used methods in real estate analysis. While the merits of combining these models when constructing house price indices are well documented, research on the utility of adopting the same approach for residential property valuation has not been conducted to date. Specifically, house value estimates were obtained by combining predictions from repeat sales and various hedonic regression specifications, which were enhanced to account for spatial effects. Three of these enhancements—regression kriging, mixed regressive-spatial autoregressive, and geographically weighted regression—are widely utilized spatial econometric models. However, a fourth augmentation, which addresses systematic residual patterns in regressions with district indicator variables and the presence of outliers in housing data, was also proposed. The resulting models were applied to a dataset containing 16,417 real estate transactions in Oslo, Norway, revealing that when the repeat sales approach is included, it reduces the median absolute percentage error of solely hedonic models by 6.8–9.5%, where greater improvements are associated with less accurate spatial enhancements. These improvements can be attributed to the inclusion of both spatial and non-spatial information inherent in previous sales prices. While the former has limited utility for well-specified spatial models, the non-spatial information that is implicit in previous sales prices likely captures otherwise difficult to observe phenomena, potentially making its contribution highly valuable in automated valuation models.

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Notes

  1. Extensive literature review has failed to uncover any publicly available research on this topic. However, some companies advertise automated valuation based on both models, e.g., Home Value Explorer® by FreddieMac (2017).

  2. FINN covers approximately 70% of the Norwegian housing market (Norge et al. 2017). All properties in the dataset employed in the current investigation were announced on the site.

  3. In Norway, cooperatives and apartment buildings can take on common debt, for example, to renovate the building. Especially for cooperatives, the common debt can be high compared with the transaction price of an apartment. The total price of a dwelling in Norway is the transaction price, plus the dwellings share of the total common debt.

  4. The term simple kriging is used when the mean of the dependent variable is assumed to be known a priori (Cressie 1990).

  5. Either administrative or generated by k-means, depending on the variable type required in the regression.

  6. By testing different weighting and combinations we found no single optimal solution for multiple performance metrics, resulting in our choice of a “trail-and-error” based weight of 60% for the regression estimate that resulted on both high prediction and low volatility across multiple runs.

  7. Generally, model performance is measured by median absolute percentage error (Q0.5).

  8. Row 13 in Figure 4 shows unsatisfactory performance by VRT where district variables are omitted.

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Authors and Affiliations

  1. NTNU Business School, Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Are Oust

  2. Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Trondheim, Norway

    Simen N. Hansen & Tobias R. Pettrem

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  1. Are Oust
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Appendix

Appendix

Table 8 K-means results with varying k, compared to administrative districts
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Table 9 Independent repeat sales results
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Fig. 6
figure 6

Out-of-sample residuals from the hedonic regression without spatial enhancements or district indicators (referred to in Section 5 as the benchmark model) depicted on the map of Oslo. Lines represent administrative district boundaries, while marker color indicates residual value for each dwelling. Observations: 3284

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Fig. 7
figure 7

Out-of-sample residuals from the hedonic regression with administrative district indicators depicted on the map of Oslo. Lines represent administrative district boundaries, whereas marker color indicates residual value for each dwelling. Observations: 3284

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Fig. 8
figure 8

Out-of-sample residuals from the most accurate model that combines geographically weighted regression and repeat sales depicted on the map of Oslo. Lines represent administrative district boundaries, whereas marker color denotes residual value for each dwelling. Observations: 3284

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Oust, A., Hansen, S.N. & Pettrem, T.R. Combining Property Price Predictions from Repeat Sales and Spatially Enhanced Hedonic Regressions. J Real Estate Finan Econ 61, 183–207 (2020). https://doi.org/10.1007/s11146-019-09723-x

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