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A mean-variance optimization approach for residential real estate valuation

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A mean-variance optimization approach for residential real estate valuation

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dc.contributor.author Guijarro, Francisco es_ES
dc.date.accessioned 2022-07-20T18:05:52Z
dc.date.available 2022-07-20T18:05:52Z
dc.date.issued 2021-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/184564
dc.description.abstract [EN] This paper introduces a new approach to the sales comparison model for the valuation of real estate that can objectively estimate the coefficients associated with the explanatory price variables. The coefficients of the price adjustment process are estimated from the formulation of a quadratic programming model similar to the mean-variance model in the portfolio selection problem and are shown to be independent of the property to be valued. It is also shown that the sales comparison model should minimize the variance of the adjusted prices, and not their coefficient of variation as indicated by some national and international valuation regulations. The paper concludes with a case study on the city of Medellin, Colombia. es_ES
dc.language Inglés es_ES
dc.publisher Towarzystwo Naukowe Nieruchomosci es_ES
dc.relation.ispartof Real Estate Management and Valuation es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Pricing es_ES
dc.subject Economic valuation es_ES
dc.subject Multiple linear regression model es_ES
dc.subject Quadratic programming es_ES
dc.subject Objective weights es_ES
dc.subject.classification ECONOMIA FINANCIERA Y CONTABILIDAD es_ES
dc.title A mean-variance optimization approach for residential real estate valuation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.2478/remav-2021-0018 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Economía y Ciencias Sociales - Departament d'Economia i Ciències Socials es_ES
dc.description.bibliographicCitation Guijarro, F. (2021). A mean-variance optimization approach for residential real estate valuation. Real Estate Management and Valuation. 29(3):13-28. https://doi.org/10.2478/remav-2021-0018 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.2478/remav-2021-0018 es_ES
dc.description.upvformatpinicio 13 es_ES
dc.description.upvformatpfin 28 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 29 es_ES
dc.description.issue 3 es_ES
dc.identifier.eissn 2300-5289 es_ES
dc.relation.pasarela S\421228 es_ES
dc.description.references Ahn, J. J., Byun, H. B., Oh, K. Y., & Kim, Y. K. (2012). Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting. Expert Systems with Applications, 39, 8369–8379. https://doi.org/10.1016/j.eswa.2012.01.18310.1016/j.eswa.2012.01.183 es_ES
dc.description.references 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.2011.08.07710.1016/j.eswa.2011.08.077 es_ES
dc.description.references Appraisal Institute. (1996). The appraisal of real estate. Appraisal Institute. es_ES
dc.description.references Arribas, I., García, F., Guijarro, F., Oliver, J., & Tamosiuniene, R. (2016). Mass appraisal of residential real estate using multilevel modelling. International Journal of Strategic Property Management, 20(1), 77–87. https://doi.org/10.3846/1648715X.2015.113470210.3846/1648715X.2015.1134702 es_ES
dc.description.references Aznar, J., Ferrís-Oñate, J., & Guijarro, F. (2010). An ANP framework for property pricing combining quantitative and qualitative attributes. The Journal of the Operational Research Society, 61(5), 740–755. https://doi.org/10.1057/jors.2009.3110.1057/jors.2009.31 es_ES
dc.description.references Aznar, J., & Guijarro, F. (2007). Estimating regression parameters with imprecise input data in an appraisal context. European Journal of Operational Research, 176(3), 1896–1907. https://doi.org/10.1016/j.ejor.2005.10.02910.1016/j.ejor.2005.10.029 es_ES
dc.description.references Aznar, J., Guijarro, F., & Moreno-Jiménez, J. M. (2011). Mixed valuation methods: A combined AHPGP procedure for individual and group multicriteria agricultural valuation. Annals of Operations Research, 190(1), 221–238. https://doi.org/10.1007/s10479-009-0527-210.1007/s10479-009-0527-2 es_ES
dc.description.references Baldominos, A., Blanco, I., Moreno, A. J., Iturrarte, R., Bernárdez, Ó., & Afonso, C. (2018). Identifying real estate opportunities using machine learning. Applied Sciences (Basel, Switzerland), 8(11), 2321. https://doi.org/10.3390/app811232110.3390/app8112321 es_ES
dc.description.references Brown, K., & Uyar, B. (2004). A hierarchical linear model approach for assessing the effects of house and neighborhood characteristics on housing prices. Journal of Real Estate Practice and Education, 7(1), 15–24. https://doi.org/10.1080/10835547.2004.1209160310.1080/10835547.2004.12091603 es_ES
dc.description.references Cupal, M., Sedlacik, M., & Michalek, J. (2019). The assessment of a building’s insurable value using multivariate statistics: The case of the Czech Republic. Real Estate Management and Valuation, 27(3), 81–96. https://doi.org/10.2478/remav-2019-002710.2478/remav-2019-0027 es_ES
dc.description.references d’Amato, M. (2002). Appraising property with rough set theory. Journal of Property Investment & Finance, 20(4), 406–418. https://doi.org/10.1108/1463578021043507410.1108/14635780210435074 es_ES
dc.description.references 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.2004.963751810.3846/1648715X.2004.9637518 es_ES
dc.description.references d’Amato, M. (2007). Comparing rough set theory with multiple regression analysis as automated valuation methodologies. International Real Estate Review, 10(2), 42–65. es_ES
dc.description.references Dmytrow, K., & Gnat, S. (2019). Application of AHP method in assessment of the influence of attributes on value in the process of real estate valuation. Real Estate Management and Valuation, 27(4), 15–26. https://doi.org/10.2478/remav-2019-003210.2478/remav-2019-0032 es_ES
dc.description.references Ebru, C., & Eban, A. (2011). Determinants of house prices in Istanbul: A quantile regression approach. Quality & Quantity, 45(2), 305–317. https://doi.org/10.1007/s11135-009-9296-x10.1007/s11135-009-9296-x es_ES
dc.description.references Eckert, J. K., Gloudemans, R. J., & Almy, R. R. (1990). Property appraisal and assessment administration. International Association of Assessing Officers. es_ES
dc.description.references Fan, G. Z., Ong, S. E., & Koh, H. C. (2006). Determinants of house price: A decision tree approach. Urban Studies (Edinburgh, Scotland), 43(12), 2301–2315. https://doi.org/10.1080/0042098060099092810.1080/00420980600990928 es_ES
dc.description.references García, N., Gámez, M., & Alfaro, E. (2008). ANN+GIS: An automated system for property valuation. Neurocomputing, 71(4-6), 733–742. https://doi.org/10.1016/j.neucom.2007.07.03110.1016/j.neucom.2007.07.031 es_ES
dc.description.references Gu, J., Zhu, M., & Jiang, L. (2011). Housing price forecasting based on genetic algorithm and support vector machine. Expert Systems with Applications, 38(4), 3383–3386. https://doi.org/10.1016/j.eswa.2010.08.12310.1016/j.eswa.2010.08.123 es_ES
dc.description.references Guijarro, F. (2019). Assessing the impact of road traffic externalities on residential price values: A case study in Madrid, Spain. International Journal of Environmental Research and Public Health, 16(24), 5149. https://doi.org/10.3390/ijerph16245149 PMID:3186105510.3390/ijerph16245149 es_ES
dc.description.references Hausler, J., Ruscheinsky, J., & Lang, M. (2018). News-based sentiment analysis in real estate: A machine learning approach. Journal of Property Research, 35(4), 344–371. https://doi.org/10.1080/09599916.2018.155192310.1080/09599916.2018.1551923 es_ES
dc.description.references Hu, L., He, S., Han, Z., Xiao, H., Su, S., Weng, M., & Cai, Z. (2019). Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies. Land Use Policy, 82, 657–673. https://doi.org/10.1016/j.landusepol.2018.12.03010.1016/j.landusepol.2018.12.030 es_ES
dc.description.references Guo, J., Xu, S., & Bi, Z. (2013). An integrated cost-based approach for real estate appraisals. Information Technology and Management, 15(2), 131–139. https://doi.org/10.1007/s10799-012-0152-710.1007/s10799-012-0152-7 es_ES
dc.description.references IAAO. (2003). Standard on automated valuation models (AVMs). International Association of Assessing Officers. es_ES
dc.description.references Kane, M. S., Linne, M. R., & Johnson, J. A. (2004). Practical Applications in Appraisal Valuation Modeling: Statistical Methods for Real Estate Practitioners. Appraisal Institute. es_ES
dc.description.references Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11(1), 443–448. https://doi.org/10.1016/j.asoc.2009.12.00310.1016/j.asoc.2009.12.003 es_ES
dc.description.references Lins, M. P. E., Novaes, L. F. L., & Legey, L. F. L. (2005). Real estate appraisal: A double perspective data envelopment analysis approach. Annals of Operations Research, 138(1), 79–96. https://doi.org/10.1007/s10479-005-2446-110.1007/s10479-005-2446-1 es_ES
dc.description.references Liu, F., Liu, D., Malekian, R., Li, Z., & Wang, D. (2017). A measurement model for real estate bubble size based on the panel data analysis: An empirical case study. PLoS One, 12(3), e0173287. https://doi.org/10.1371/journal.pone.0173287 PMID:2827314110.1371/journal.pone.0173287 es_ES
dc.description.references Liu, L., & Wu, L. (2020). Predicting housing prices in China based on modified Holt’s exponential smoothing incorporating whale optimization algorithm. Socio-Economic Planning Sciences, 72, 100916. Advance online publication. https://doi.org/10.1016/j.seps.2020.10091610.1016/j.seps.2020.100916 es_ES
dc.description.references Liu, R., & Liu, L. (2019). Predicting housing price in China based on long short-term memory incorporating modified genetic algorithm. Soft Computing, 23(22), 11829–11838. https://doi.org/10.1007/s00500-018-03739-w10.1007/s00500-018-03739-w es_ES
dc.description.references Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x10.1111/j.1540-6261.1952.tb01525.x es_ES
dc.description.references Narula, S. C., Wellington, J. F., & Lewis, S. A. (2012). Valuating residential real estate using parametric programming. European Journal of Operational Research, 217(1), 120–128. https://doi.org/10.1016/j.ejor.2011.08.01410.1016/j.ejor.2011.08.014 es_ES
dc.description.references Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert Systems with Applications, 42(6), 2928–2934. https://doi.org/10.1016/j.eswa.2014.11.040; https://doi.org/10.1016/j.eswa.2015.03.005 es_ES
dc.description.references Pérez-Rave, J. I., Correa-Morales, J. C., & González-Echavarría, F. (2019). A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes. Journal of Property Research, 36(1), 59–96. https://doi.org/10.1080/09599916.2019.158748910.1080/09599916.2019.1587489 es_ES
dc.description.references Plakandaras, V., Gupta, R., Gogas, P., & Papadimitriou, T. (2015). Forecasting the US real house price index. Economic Modelling, 45, 259–267. https://doi.org/10.1016/j.econmod.2014.10.05010.1016/j.econmod.2014.10.050 es_ES
dc.description.references Raslanas, S., Zavadskas, E. K., Kaklauskas, A., & Zabulenas, A. R. (2010). Land value tax in the context of sustainable urban development and assessment. Part II - Analysis of land valuation techniques: The case of Vilnius. International Journal of Strategic Property Management, 14(2), 173–190. https://doi.org/10.3846/ijspm.2010.1310.3846/ijspm.2010.13 es_ES
dc.description.references Selim, H. (2009). Determinants of house prices in turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), 2843–2852. https://doi.org/10.1016/j.eswa.2008.01.04410.1016/j.eswa.2008.01.044 es_ES
dc.description.references Valier, A. (2020). Who performs better? AVMs vs hedonic models. Journal of Property Investment & Finance, 38(3), 213–225. https://doi.org/10.1108/JPIF-12-2019-015710.1108/JPIF-12-2019-0157 es_ES
dc.description.references Wiltshaw, D. (1995). A comment on methodology and valuation. Journal of Property Research, 12(2), 157–161. https://doi.org/10.1080/0959991950872413910.1080/09599919508724139 es_ES
dc.description.references Wu, C., Ye, X., Ren, F., Wan, Y., Ning, P., & Du, Q. (2016). Spatial and social media data analytics of housing prices in Shenzhen, China. PLoS One, 11(10), e0164553. https://doi.org/10.1371/journal.pone.0164553 PMID:2778364510.1371/journal.pone.0164553 es_ES


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