Sales Time Estimation Methodology Through Survival Models
Authors
-
David Sánchez-Cabrera
Universidad Católica Santa Teresa de Jesús de Ávila
https://orcid.org/0009-0009-5034-9300
-
Julio González-Arias
Universidad Nacional de Educación a Distancia
https://orcid.org/0000-0003-4993-4739
-
David Rey-Blanco
Idealista
https://orcid.org/0000-0002-3549-1714
Abstract
Knowing in detail those factors that are most relevant to the probability of selling homes can be of utmost importance, among other things, as it is a long-term investment. After identifying a series of blocks that mark the evolution of their sale, such as the internal characteristics of the property in question, its location, its degree of overpricing and, above all, a real interest on the part of the buyer, a novel study is presented that models the probability of selling properties over time using machine learning techniques applied to survival problems, achieving C-index values of 76% and 72% in chalets and apartments, respectively. The methodological process has been tested in the capital of Spain, Madrid, based on data collected from the country's main market platform during the 2018-2019 period, weighted according to official information, but the methodology is scalable to any municipality. Not only sellers, buyers or intermediaries can benefit from this contribution, but also public agents in order to make decisions focused on design or prevention in the area of housing.
Keywords:
housing market, price and valuation of the home, survival analysis applied to home sales, time-dependent selling probability, time on market (TOM)References
Ahmed, A., Ardila, D., Sanadgol, D., y Sornette, D. (2016). Comparing ask and transaction prices in the Swiss housing market. Swiss Finance Institute Research Paper Series, (16-80). https://doi.org/10.2139/ssrn.2894404
An, Z., Cheng, P., Lin, Z., y Liu, Y. (2013). How do market conditions impact price-TOM relationship? Evidence from real estate owned (REO) sales. Journal of Housing Economics, 22(3), 250-263. https://doi.org/10.1016/j.jhe.2013.07.003
Anglin, P. M., Rutherford, R., y Springer, T. M. (2001). The trade-off between the selling price of residential properties and time-on-the-market: The impact of price setting. The Journal of Real Estate Finance and Economics, 26, 95–111. https://doi.org/10.1023/A:1021526332732
Ayuntamiento de Madrid. (2020). Compra-venta de viviendas. https://www.madrid.es/portales/munimadrid/es/Inicio/El-Ayuntamiento/Estadistica/Areas-de-informacion-estadistica/Edificacion-y-vivienda/Mercado-de-la-vivienda/Compra-venta-de-viviendas/?vgnextfmt=default&vgnextoid=9b8db9602f841510VgnVCM1000000b205a0aRCRD&vgnextchannel=22613c7ea422a210VgnVCM1000000b205a0aRCRD
Baldominos, A., Blanco, I., Moreno, A. J., Iturrarte, R., Bernárdez, Ó., y Afonso, C. (2018). Identifying real estate opportunities using machine learning. 8(11), 2321. https://doi.org/10.3390/app8112321
Bhuiyan, M. y Hasan, M. (2016). Waiting to be sold: Prediction of time-dependent house selling probability. 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (DSAA), 468-477. https://doi.org/10.1109/DSAA.2016.58
Bich, H. N., Trong, H. N., y Thanh, H. T. (2020). The role of listing price strategies on the probability of selling a house: Evidence from Vietnam. Real Estate Management and Valuation, 28(2), 63-75. https://doi.org/10.1515/remav-2020-0016
Chen, Y. y Rosenthal, R. W. (1996). On the use of ceiling-price commitments by monopolists. The RAND Journal of Economics, 27(2), 207-220. https://www.jstor.org/stable/2555923
Choy, L. H. T. y Ho, W. K. O. (2023). The use of machine learning in real estate research. Land, 12(4), 740. https://doi.org/10.3390/land12040740
Cirman, A., Pahor, M., y Verbic, M. (2015). Determinants of time on the market in a thin real estate market. Engineering Economics, 26(1). https://doi.org/10.5755/j01.ee.26.1.3905
Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 34(2), 187-202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x
Deville, J.-C., Särndal, C.-E., y Sautory, O. (1993). Generalized raking procedures in survey sampling. Journal of the American Statistical Association, 88(423), 1013-1020. https://doi.org/10.1080/01621459.1993.10476369
Eurostat. (2019). Distribution of population by tenure status, type of household and income group - EU-SILC survey [datos]. https://ec.europa.eu/eurostat/databrowser/view/ilc_lvho02/default/table?lang=en
Forgey, F. A., Rutherford, R. C., y Springer, T. M. (1996). Search and liquidity in single‐family housing. Real Estate Economics, 24(3), 273-292. https://doi.org/10.1111/1540-6229.00691
Geltner, D., Kluger, B. D., y Miller, N. G. (1991). Optimal price and selling effort from the perspectives of the broker and seller. Real Estate Economics, 19(1), 1-24. https://doi.org/10.1111/1540-6229.00537
Genesove, D. y Mayer, C. (1997). Equity and time to sale in the real estate market. The American Economic Review, 87(3), 255-269.
Glower, M., Haurin, D. R., y Hendershott, P. H. (1998). Selling time and selling price: the impact of seller motivation. Real Estate Economics, 26(4), 719-740. https://doi.org/10.1111/1540-6229.00763
Ministerio de Transportes y Movilidad Sostenible. (2023). Transacciones inmobiliarias (compraventa) [datos]. https://apps.fomento.gob.es/BoletinOnline2/?nivel=2&orden=34000000
Han, L. y Strange, W. C. (2016). What is the role of the asking price for a house? Journal of Urban Economics, 93, 115-130. https://doi.org/10.1016/j.jue.2016.03.008
Harrell, F., Califf, R., Pryor, D., Lee, K., y Rosati, R. (1982). Evaluating the yield of medical tests. JAMA, 247(18), 2543-2546. https://doi.org/10.1001/jama.1982.03320430047030
Haurin, D. (1988). The duration of marketing time of residential housing. Real Estate Economics, 16(4), 396-410. https://doi.org/10.1111/1540-6229.00463
Instituto Nacional de Estadística. (2002a). Cambios metodológicos EPA-2002. Cifras INE. Boletín Informativo del Instituto Nacional de Estadísticas, (3). https://www.ine.es/epa02/cifrasine_epa02.pdf
Instituto Nacional de Estadística. (2002b). Método de reponderación aplicado en la EPA. https://www.ine.es/epa02/documento_tecnico.pdf
Instituto Nacional de Estadística. (2019). Hogares por régimen de tenencia de la vivienda y tipo de hogar. Autor. https://www.ine.es/jaxiT3/Tabla.htm?t=9996&L=0
Instituto Nacional de Estadística. (2023a). Estadística de transmisiones de derechos de la propiedad. Compraventa de viviendas según régimen y estado. Autor. https://www.ine.es/jaxiT3/Tabla.htm?t=6150&L=0
Instituto Nacional de Estadística. (2023b). Series históricas de población desde 1996. Cifras oficiales de la Revisión anual del Padrón municipal a 1 de enero de cada año. Autor. https://www.ine.es/jaxiT3/Tabla.htm?t=29005&L=0
International Valuation Standards Council. (2022). International valuation standards. Autor. https://www.rics.org/content/dam/ricsglobal/documents/standards/ivsc_effective_31_jan_2022.pdf
Johnson, K., Benefield, J., y Wiley, J. (2007). The probability of sale for residential real estate. Journal of Housing Research, 16(2), 131-142. https://doi.org/10.1080/10835547.2007.12091978
Kang, H. B. y Gardner, M. J. (1989). Selling price and marketing time in the residential real estate market. Journal of Real Estate Research, 4(1), 21-35. https://doi.org/10.1080/10835547.1989.12090570
Kaplan, E. L. y Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481. https://doi.org/10.1080/01621459.1958.10501452
Khezr, P. (2015). Time on the market and price change: the case of Sydney housing market. Applied Economics, 47(5), 485-498. https://doi.org/10.1080/00036846.2014.972549
Kleinbaum, D. y Klein, M. (1996). Survival analysis: a self-learning text. Springer.
Knight, J. R. (2002). Listing price, time on market, and ultimate selling price: Causes and effects of listing price changes. Real Estate Economics, 30(2), 213-237. https://doi.org/10.1111/1540-6229.00038
Lazear, E. (1986). Retail pricing and clearance sales. The American Economic Review, 76(1), 14–32.
Li, W.-F. (2015). The impact of pricing on time-on-market in high-rise multiple-unit residential developments. Pacific Rim Property Research Journal, 10(3), 305-327. https://doi.org/10.1080/14445921.2004.11104165
Mantel, N. (1966). Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemotherapy Reports, 50(3), 163-70.
McCall, J. J. y Lippman, S. A. (1984). An operational measure of liquidity. University of Konstanz, Department of Economics.
Ngai, L. y Tenreyro, S. (2014). Hot and cold seasons in the housing market. The American Economic Review, 104(12), 3991–4026.
Oxford Spring School. (2007). An introduction to event history analysis. https://spia.uga.edu/faculty_pages/rbakker/pols8501/OxfordTwoNotes.pdf
Pölsterl, S. (2020). scikit-survival: A library for time-to-event analysis built on top of scikit-learn. Journal of Machine Learning Research, 21(212), 1–6.
Quan, D. C. y Quigley, J. M. (1991). Price formation and the appraisal function in real estate markets. The Journal of Real Estate Finance and Economics, 4(2). https://doi.org/10.1007/bf00173120
Scofield, D. y Devaney, S. (2017). What sells in a crisis? Determinants of sale probability over a cycle and through a crash. Journal of Property Investment & Finance, 35(6), 619-637. https://doi.org/10.1108/jpif-02-2017-0013
Selcuk, C. (2012). Motivated sellers predatory buyers. Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/36226/
Vergara-Perucich, J. (2021). Precios y financierización: evidencia empírica en mercado de la vivienda del Gran Santiago. Revista INVI, 36(103), 137–166. https://doi.org/10.4067/S0718-83582021000300137
Twitter
Facebook
