Path selection of spatial econometric model for mass appraisal of real estate: evidence from Yinchuan, China
Abstract
Urbanization, national economic growth, and China’s changing population structure have elevated the importance of real estate assessment in various contexts, including mortgage financing, secondary housing market transactions, and real estate tax reform. To address this need, this study employs a time-spatial double-fixed spatial cross-section data model as a mass appraisal tool to analyze the transaction price data of 429 ordinary residential houses in Xixia District, Yinchuan, China on April 1, 2022. Specifically, this study analyzes 7 spatial cross-section data models, discerning their interconnections. It devises an assignment technique that merges distance and characteristic variable rank into a unified indicator. The results explore spatial lag effects in real estate transaction price generation and assess the descriptive capabilities of different spatial cross-section data models.
Keyword : mass appraisal of real estate, spatial section data model, SDM, empirical analysis
How to Cite
Zhao, Y., Shen, X., Ma, J., & Yu, M. (2023). Path selection of spatial econometric model for mass appraisal of real estate: evidence from Yinchuan, China. International Journal of Strategic Property Management, 27(5), 304–316. https://doi.org/10.3846/ijspm.2023.20376
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Nellis, J. G., & Longbottom, J. A. (1981). An empirical analysis of the determination of house prices in the United Kingdom. Urban Studies, 18(1), 9–21. https://doi.org/10.1080/00420988120080021
Osland, L. (2010). An application of spatial econometrics in relation to hedonic house price modeling. Journal of Real Estate Research, 32(3), 289–320. https://doi.org/10.1080/10835547.2010.12091282
Pace, R. K. (1995). Parametric, semiparametric, and nonparametric estimation of characteristic values within mass assessment and hedonic pricing models. The Journal of Real Estate Finance and Economics, 11(3), 195–217. https://doi.org/10.1007/BF01099108
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Wilhelmsson, M. (2002). Spatial models in real estate economics. Housing, Theory and Society, 19(2), 92–101. https://doi.org/10.1080/140360902760385646
Williams, R. M. (1955). The relationship of housing prices and building costs in Los Angeles, 1900–1953. Journal of the American Statistical Association, 50(270), 370–376. https://doi.org/10.1080/01621459.1955.10501271
Yasnitsky, L. N., Yasnitsky, V. L., & Alekseev, A. O. (2021). The complex neural network model for mass appraisal and scenario forecasting of the urban real estate market value that adapts itself to space and time. Complexity, 2021, 5392170. https://doi.org/10.1155/2021/5392170
Antipov, E. A., & Pokryshevskaya, E. B. (2012). Mass appraisal of residential apartments: an application of random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39(2), 1772–1778. https://doi.org/10.1016/j.eswa.2011.08.077
Blettner, R. A. (1969). Mass appraisals via multiple regression analysis. The Appraisal Journal, 37(4), 513–521. https://doi.org/10.1111/j.1467-6494.1969.tb01761.x
Brankovic, S. (2013). Real estate mass appraisal in the real estate cadastre and GIS environment. Geodetski List, 67(2), 119–134.
Carbone, R., & Longini, R. L. (1977). A feedback model for automated real estate assessment. Management Science, 24(3), 241–248. https://doi.org/10.1287/mnsc.24.3.241
Chen, L., Wei, Y., & Yang, X. (2020). An integrated machine learning approach for real estate appraisal: a case study of Shanghai, China. Sustainability, 12(4), 1564.
D’amato, M. (2004). A comparison between MRA and rough set theory for mass appraisal. A case in Bari. International Journal of Strategic Property Management, 8(4), 205–217. https://doi.org/10.3846/1648715X.2004.9637518
Dimopoulos, T., Tyralis, H., Bakas, N. P., & Hadjimitsis, D. (2018). Accuracy measurement of random forests and linear regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus. Advances in Geosciences, 45, 377–382. https://doi.org/10.5194/adgeo-45-377-2018
Doszyń, M. (2020). Algorithm of real estate mass appraisal with inequality restricted least squares (IRLS) estimation. Journal of European Real Estate Research, 13(2), 161–179. https://doi.org/10.1108/JERER-11-2019-0040
Dubin, R., Pace, R. K., & Thibodeau, T. G. (1999). Spatial autoregression techniques for real estate data. Journal of Real Estate Literature, 7(1), 79–96. https://doi.org/10.1080/10835547.1999.12090079
Elhorst, J. P. (2010). Applied spatial econometrics: raising the bar. Spatial Economic Analysis, 5(1), 9–28. https://doi.org/10.1080/17421770903541772
Feng, Y., Zhang, H., & Liu, Y. (2019). Spatial-temporal autoregressive model for real estate mass appraisal. Mathematical Problems in Engineering, 2019, 4267532. https://doi.org/10.1155/2019/4267532
Fletcher, M., Gallimore, P., & Mangan, J. (2000). Heteroscedasticity in hedonic house price models. Journal of Property Research, 17(2), 93–108. https://doi.org/10.1080/095999100367930
Gloudemans, R. J. (2002). Comparison of three residential regression models: additive, multiplicative, and nonlinear. Assessment Journal, 9(4), 25–36.
Hermans, L. D., Bidanset, P. E., Davis, P. T., & McCord, M. J. (2022). Using property-level ratio studies for the incorporation of validation models in single-family residential real estate assessment. Journal of Property Tax Assessment & Administration, 19(1), 83–102.
Hermans, L. D., McCord, M. J., Davis, P. T., & Bidanset, P. E. (2023). An exploratory approach to composite modelling for real estate assessment and accuracy. Journal of Property Tax Assessment & Administration, 20(1). https://researchexchange.iaao.org/jptaa/vol20/iss1/4
International Association of Assessing Officers. (2012). Standard on mass appraisal of real property. https://www.iaao.org/media/standards/StandardOnMassAppraisal.pdf
International Association of Assessing Officers. (2013). Standard on ratio studies. https://www.iaao.org/media/standards/Standard_on_Ratio_Studies.pdf
Kanji, G. K. (1975). Analysis of the relative importance of some factors affecting house prices in a local market in 1970. International Journal of Mathematical Education in Science and Technology, 6(3), 277–282. https://doi.org/10.1080/0020739750060303
Lo, D., Chau, K. W., Wong, S. K., McCord, M., & Haran, M. (2022). Factors affecting spatial autocorrelation in residential property prices. Land, 11(6), 931. https://doi.org/10.3390/land11060931
McCluskey, W., Deddis, W., Mannis, A., McBurney, D., & Borst, R. (1997). Interactive application of computer assisted mass appraisal and geographic information systems. Journal of Property Valuation and Investment, 15(5), 448–465. https://doi.org/10.1108/14635789710189227
McCord, M., Lo, D., Davis, P., McCord, J., Hermans, L., & Bidanset, P. (2022). Applying the geostatistical eigenvector spatial filter approach into regularized regression for improving prediction accuracy for mass appraisal. Applied Sciences, 12(20), 10660. https://doi.org/10.3390/app122010660
Nellis, J. G., & Longbottom, J. A. (1981). An empirical analysis of the determination of house prices in the United Kingdom. Urban Studies, 18(1), 9–21. https://doi.org/10.1080/00420988120080021
Osland, L. (2010). An application of spatial econometrics in relation to hedonic house price modeling. Journal of Real Estate Research, 32(3), 289–320. https://doi.org/10.1080/10835547.2010.12091282
Pace, R. K. (1995). Parametric, semiparametric, and nonparametric estimation of characteristic values within mass assessment and hedonic pricing models. The Journal of Real Estate Finance and Economics, 11(3), 195–217. https://doi.org/10.1007/BF01099108
Song, H. (2021). Research on the impact of COVID-19 on China’s real estate industry. In 2021 6th International Conference on Social Sciences and Economic Development (ICSSED 2021) (pp. 401–406). Atlantis Press. https://doi.org/10.2991/assehr.k.210407.078
Wilhelmsson, M. (2002). Spatial models in real estate economics. Housing, Theory and Society, 19(2), 92–101. https://doi.org/10.1080/140360902760385646
Williams, R. M. (1955). The relationship of housing prices and building costs in Los Angeles, 1900–1953. Journal of the American Statistical Association, 50(270), 370–376. https://doi.org/10.1080/01621459.1955.10501271
Yasnitsky, L. N., Yasnitsky, V. L., & Alekseev, A. O. (2021). The complex neural network model for mass appraisal and scenario forecasting of the urban real estate market value that adapts itself to space and time. Complexity, 2021, 5392170. https://doi.org/10.1155/2021/5392170