Real estate valuation models performance in price prediction
Abstract
Using a sample of 900 apartments from Cluj-Napoca, Romania, containing selling transactions for the second semester of 2019, and data for 33 locational, physical and neighbourhood-related attributes (socio-cultural, environmental, and urbanism related), our research objective is to test the performance in price prediction, and hence the utility, of the Artificial Neural Networking (ANN), as artificial intelligence model versus the Generalized Linear Model (GLM), as a regression model. By contributing to an ongoing debate, our empirical findings confirm the results of a predominant group of earlier studies, namely the superiority of ANN. Precisely, we found that ANN can better predict selling prices and provides stability of results. Additionally, we addressed the critiques related to the transparency of results, showing that ANN also has the ability to illustrate the significance of the different attributes of real estate, if appropriate statistical indicators are used. These findings can serve the different real estate valuation purposes, including that of the review of valuation reports.
Keyword : real estate, valuation reports review, artificial neural networking, price prediction
How to Cite
Deaconu, A., Buiga, A., & Tothăzan, H. (2022). Real estate valuation models performance in price prediction. International Journal of Strategic Property Management, 26(2), 86-105. https://doi.org/10.3846/ijspm.2022.15962
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abidoye, R. B., & Chan, A. P. C. (2017). Artificial neural network in property valuation: application framework and research trend. Property Management, 35(5), 554–571. https://doi.org/10.1108/PM-06-2016-0027
Abidoye, R. B., & Chan, A. P. C. (2018). Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network. Pacific Rim Property Research Journal, 24, 71–83. https://doi.org/10.1080/14445921.2018.1436306
Abraham, J. M., & Hendershott, P. H. (1996). Bubbles in metropolitan housing markets. Journal of Housing Research, 7(2), 191–207. https://doi.org/10.3386/w4774
Allen, W. C., & Zumwalt, J. K. (1994). Neural networks: a word of caution (Working Paper). Colorado State University.
American Society of Appraisers. (2004). Valuing machinery and equipment. ANEVAR.
Appraisal Institute. (2001). The appraisal of real estate (12th ed.). Chicago.
Association of the Romanian Valuators. (2020). Standards for the goods valuation (SEV). ANEVAR. http://site2.anevar.ro/sites/default/files/page-files/standarde_2020_dupa_cn_27_iulie_final_31.07.2020.pdf
Benjamin, J. D., Guttery, R. S., & Sirmans, C. F. (2004). Mass Appraisal: an introduction to multiple regression analysis for real estate valuation. Journal of Real Estate Practice and Education, 7(1), 65–78. https://doi.org/10.1080/10835547.2004.12091602
Blackley, D. M., Follain, J. R., & Lee, H. (1986). An evaluation of hedonic price indexes for thirty-four large SMSAs. American Real Estate and Urban Economics Association Journal, 14(2), 179–205. https://doi.org/10.1111/1540-6229.00382
Bogin, A. N., & Shui, J. (2020). Appraisal accuracy and automated valuation models in rural areas. The Journal of Real Estate Finance and Economics, 60, 40–52. https://doi.org/10.1007/s11146-019-09712-0
Borst, R. A. (1991). Artificial neural networks: the next modelling/calibration technology for the assessment community. Property Tax Journal, 10(1), 69–94.
Brunson, A., Buttimer, R. J., & Rutherford, R. (1994). Neural networks, nonlinear specifications, and industrial property values (Working paper series). University of Texas at Arlington, Arlington, TX.
Cechin, A., Souto, A., & Gonzales, A. M. (2000). Real estate value at Porto Alegre city using artificial neural networks. In Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks (pp. 237–242). IEEE. https://doi.org/10.1109/SBRN.2000.889745
Chiarazzo, V., Caggiani, L., Marinelli, M., & Ottomanelli, M. (2014). A neural network based model for real estate price estimation considering environmental quality of property location. Transportation Research Procedia, 3, 810–817. https://doi.org/10.1016/j.trpro.2014.10.067
Curry, B., Morgan, P., & Silver, M. (2002). Neural networks and non-linear statistical methods: an application to the modelling of price–quality relationships. Computers & Operations Research, 29(8), 951–969. https://doi.org/10.1016/S0305-0548(00)00096-4
Din, A., Hoesli, M., & Bender, A. (2001). Environmental variables and real estate prices. Urban Studies, 38(11), 1989–2000. https://doi.org/10.1080/00420980120080899
DiPasquale, D., & Wheaton, W. C. (1994). Housing market dynamics and the future of housing prices. Journal of Urban Economics, 35(1), 1–27. https://doi.org/10.1006/juec.1994.1001
Do, A. Q., & Grudnitski, G. (1992). A neural network approach to residential property appraisal. The Real Estate Appraiser, 58(3), 38–45.
Evans, A., James, H., & Collins, A. (1992). Artificial neural networks: an application to residential valuation in the UK. Journal of Property Valuation and Investment, 11, 195–203.
Fabozzi, F. J., Shiller, R. J., & Tunaru, R. S. (2010). Property derivatives for managing European real-estate risk. European Financial Management, 16(1), 8–26. https://doi.org/10.1111/j.1468-036X.2009.00528.x
Fox, G. (2008). Applied regression analysis and generalized linear models. Sage Publications.
Ghysels, E., Plazzi, A., & Valkanov, R. (2007). Valuation in US commercial real estate. European Financial Management, 13(3), 472–497. https://doi.org/10.1111/j.1468-036X.2007.00369.x
Glumac, B., & Des Rosiers, F. (2021a). Practice briefing – Automated valuation models (AVMs): their role, their advantages and their limitations. Journal of Property Investments & Finance, 39(5), 481–491. https://doi.org/10.1108/JPIF-07-2020-0086
Glumac, B., & Des Rosiers, F. (2021b). Towards a taxonomy for real estate and land automated valuation systems. Journal of Property Investments & Finance, 39(5), 450–463. https://doi.org/10.1108/JPIF-07-2020-0087
Helbich, M., Brunauer, W., Vaz, E., & Nijkamp, P. (2014). Spatial heterogeneity in hedonic house price models: the case of Austria. Urban Study, 51, 390–411. https://doi.org/10.1177/0042098013492234
Ho, W. K. O., Tang, B. S., & Wong, S. W. (2020). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48–70. https://doi.org/10.1080/09599916.2020.1832558
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 modelling to inform equitable housing policies. Land Use Policy, 82, 657–673. https://doi.org/10.1016/j.landusepol.2018.12.030
Ibeas, T., Cordera, R., dell’Olio, L., Coppola, P., & Dominquez, A. (2012). Modelling transport and real-estate values interactions in urban systems. Journal of Transport Geography, 24, 370–382. https://doi.org/10.1016/j.jtrangeo.2012.04.012
International Valuation Standards Committee. (2003). International Valuation Guidance Note No. 11: reviewing valuations. http://www.romacor.ro/legislatie/25-gn11.pdf
International Valuation Standards Council. (2017). International Valuation Standards. https://www.ivsc.org/standards/international-valuation-standards
Isakson, H. R. (1998). The review of real estate appraisals using multiple regression analysis. Journal of Real Estate Research, 15(1/2), 177–190. https://doi.org/10.1080/10835547.1998.12090922
Jahanshiri, E., Buyong, T., & Shariff, A. R. M. (2011). A review of property mass valuation models. Pertanika Journal of Science and Technology, 19(1), 23–30.
Jones, C. (2002). The definition of housing market area and strategic planning. Urban Studies, 39(3), 549–564. https://doi.org/10.1080/00420980220112829
Kaklauskas, A., Zavadskas, E. K., Bagdonavicius, A., Kelpsiene, L., Bardauskiene, D., & Kutut, V. (2010). Conceptual modelling of construction and real estate crisis with emphasis on comparative qualitative aspects descriptions. Transformations in Business and Economics, 9(1/19), 42–61.
Kauko, T., & d’Amato, M. (2008). Mass appraisal methods: an international perspective for property valuers. John Wiley & Sons. https://doi.org/10.1002/9781444301021
Kokinis-Graves, C. (2006). Use of the cost, income and sales-comparison approaches in the valuation of real estate. Journal of State Taxation, 23–32.
Kuburić, M., Tomić, H., & Mastelić Ivić, S. (2012). Use of multicriteria valuation of spatial units in a system of mass real estate valuation. KiG, 11(17), 58–74. https://hrcak.srce.hr/85966
Lai, P. Y. (2011). Analysis of the mass appraisal model by using artificial neural network in Kaohsiung city. Journal of Modern Accounting and Auditing, 7(10), 1080–1089.
Lamont, O., & Stein, J. (1999). Leverage and house price dynamics in US cities. RAND Journal of Economics, 30(3), 498–514.
Mathieson, K., & Dryer, B. J. (1993). Improving the effectiveness and efficiency of appraisal reviews: an information systems approach. Appraisal Journal, 61(3), 57–63.
Mayer, C. J., & Somerville, C. T. (2000). Land use regulation and new construction. Regional Science and Urban Economics, 30(6), 639–662. https://doi.org/10.1016/S0166-0462(00)00055-7
McCluskey, N., Davis, P., Haran, M., McCord, M., & McIlhatton, D. (2012). The potential of artificial neural networks in mass appraisal: the case revisited. Journal of Financial Management Property and Construction, 17(3), 274–292. https://doi.org/10.1108/13664381211274371
McCluskey, W. J., & Borst, R. (1997). An evaluation of MRA, comparable sales analysis and ANNs for the mass appraisal of residential properties in Northern Ireland. Assesment Journal, 4(1), 47–55.
McCulloch, C. E., & Searle, S. R. (2001). Generalized, linear, and mixed models. John Wiley & Sons. https://doi.org/10.1002/9780470057339.vag009
Mora-Esperanza, J. G. (2004). Artifical intelligence applied to real estate valuation: an example for the appraisal of Madrid. CATASTRO, 255–274.
Nguyen, N., & Cripps, A. (2001). Predicting housing value: a comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313–336. https://doi.org/10.1080/10835547.2001.12091068
Núñez Tabales, J. M., Caridad y Ocerin, J. M., & Rey Carmona, F. J. (2013). Artificial neural networks for predicting real estate prices. Revista de Metodos Cuantitativos para la Economia y la Empresa, 15(1), 29–44.
Peterson, S., & Flanagan, A. B. (2009). Neural network hedonic pricing models in mass real estate appraisal. Journal of Real Estate Research, 31(2), 147–164. https://doi.org/10.1080/10835547.2009.12091245
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55. https://doi.org/10.1086/260169
Roulac, S. E. (1986). Real estate as a strategic resource (pp. 317–321). Chief Financial Officer International.
Sampathkumar, V., Santhi, M. H., & Vanjinathan, J. (2015). Evaluation of the trend of land price using regression and neural network models. Asian Journal of Scientific Research, 8(2), 182–194. https://doi.org/10.3923/ajsr.2015.182.194
Scheurwater, S. (2017). The future of valuations: the relevance of real estate valuations for institutional investors and banks – view from a European Expert Group. RICS.
Selim, H. (2009). Determinants of house prices in Turkey: hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2/2), 2843–2852. https://doi.org/10.1016/j.eswa.2008.01.044
Stevenson, S. (2004). New empirical evidence on heteroskedasticity in hedonic housing models. Journal of Housing Economics, 13(2), 136–153. https://doi.org/10.1016/j.jhe.2004.04.004
Tay, D. P. H., & Ho, D. K. H. (1992). Artificial intelligence and the mass appraisal of residential apartments. Journal of Property Valuation and Investment, 10(2), 525–540. https://doi.org/10.1108/14635789210031181
TEGoVA. (2020). European Valuation Standards (EVS). https://tegova.org/european-valuation-standards-evs
Valier, A., & Micelli, E. (2020). Automated models for value prediction: a critical review of the debate. Valori e Valutazioni, 24, 151–162.
Vascu, A. (2015). About valuation and valuation review [Despre evaluare și verificarea evaluării]. Hamangiu / IROVAL.
Vo, N., Shi, H., & Szajman, J. (2014). Optimisation to ANN inputs in automated property valuation model with Encog 3 and winGamma. Applied Mechanics and Materials, 462–463, 1081–1086. https://doi.org/10.4028/www.scientific.net/AMM.462-463.1081
Worzala, E., Lenk, M., & Silva, A. (1995). An exploration of neural networks and its application to real estate valuation. Journal of Real Estate Research, 10(2), 185–201. https://doi.org/10.1080/10835547.1995.12090782
Xie, X., & Hu, G. (2007). A comparison of Shanghai housing price index forecasting. In 3rd International Conference on Natural Computation (ICNC) (pp. 221–225), Haikou. https://doi.org/10.1109/ICNC.2007.14
Yacim, J. A., & Boshoff, D. G. B. (2018). Combiniang BP with PSO algorithms in weights optimisation and ANNs training for mass appraisal of properties. International Journal of Housing Markets and Analysis, 11(2), 290–314. https://doi.org/10.1108/IJHMA-02-2017-0021
Abidoye, R. B., & Chan, A. P. C. (2018). Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network. Pacific Rim Property Research Journal, 24, 71–83. https://doi.org/10.1080/14445921.2018.1436306
Abraham, J. M., & Hendershott, P. H. (1996). Bubbles in metropolitan housing markets. Journal of Housing Research, 7(2), 191–207. https://doi.org/10.3386/w4774
Allen, W. C., & Zumwalt, J. K. (1994). Neural networks: a word of caution (Working Paper). Colorado State University.
American Society of Appraisers. (2004). Valuing machinery and equipment. ANEVAR.
Appraisal Institute. (2001). The appraisal of real estate (12th ed.). Chicago.
Association of the Romanian Valuators. (2020). Standards for the goods valuation (SEV). ANEVAR. http://site2.anevar.ro/sites/default/files/page-files/standarde_2020_dupa_cn_27_iulie_final_31.07.2020.pdf
Benjamin, J. D., Guttery, R. S., & Sirmans, C. F. (2004). Mass Appraisal: an introduction to multiple regression analysis for real estate valuation. Journal of Real Estate Practice and Education, 7(1), 65–78. https://doi.org/10.1080/10835547.2004.12091602
Blackley, D. M., Follain, J. R., & Lee, H. (1986). An evaluation of hedonic price indexes for thirty-four large SMSAs. American Real Estate and Urban Economics Association Journal, 14(2), 179–205. https://doi.org/10.1111/1540-6229.00382
Bogin, A. N., & Shui, J. (2020). Appraisal accuracy and automated valuation models in rural areas. The Journal of Real Estate Finance and Economics, 60, 40–52. https://doi.org/10.1007/s11146-019-09712-0
Borst, R. A. (1991). Artificial neural networks: the next modelling/calibration technology for the assessment community. Property Tax Journal, 10(1), 69–94.
Brunson, A., Buttimer, R. J., & Rutherford, R. (1994). Neural networks, nonlinear specifications, and industrial property values (Working paper series). University of Texas at Arlington, Arlington, TX.
Cechin, A., Souto, A., & Gonzales, A. M. (2000). Real estate value at Porto Alegre city using artificial neural networks. In Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks (pp. 237–242). IEEE. https://doi.org/10.1109/SBRN.2000.889745
Chiarazzo, V., Caggiani, L., Marinelli, M., & Ottomanelli, M. (2014). A neural network based model for real estate price estimation considering environmental quality of property location. Transportation Research Procedia, 3, 810–817. https://doi.org/10.1016/j.trpro.2014.10.067
Curry, B., Morgan, P., & Silver, M. (2002). Neural networks and non-linear statistical methods: an application to the modelling of price–quality relationships. Computers & Operations Research, 29(8), 951–969. https://doi.org/10.1016/S0305-0548(00)00096-4
Din, A., Hoesli, M., & Bender, A. (2001). Environmental variables and real estate prices. Urban Studies, 38(11), 1989–2000. https://doi.org/10.1080/00420980120080899
DiPasquale, D., & Wheaton, W. C. (1994). Housing market dynamics and the future of housing prices. Journal of Urban Economics, 35(1), 1–27. https://doi.org/10.1006/juec.1994.1001
Do, A. Q., & Grudnitski, G. (1992). A neural network approach to residential property appraisal. The Real Estate Appraiser, 58(3), 38–45.
Evans, A., James, H., & Collins, A. (1992). Artificial neural networks: an application to residential valuation in the UK. Journal of Property Valuation and Investment, 11, 195–203.
Fabozzi, F. J., Shiller, R. J., & Tunaru, R. S. (2010). Property derivatives for managing European real-estate risk. European Financial Management, 16(1), 8–26. https://doi.org/10.1111/j.1468-036X.2009.00528.x
Fox, G. (2008). Applied regression analysis and generalized linear models. Sage Publications.
Ghysels, E., Plazzi, A., & Valkanov, R. (2007). Valuation in US commercial real estate. European Financial Management, 13(3), 472–497. https://doi.org/10.1111/j.1468-036X.2007.00369.x
Glumac, B., & Des Rosiers, F. (2021a). Practice briefing – Automated valuation models (AVMs): their role, their advantages and their limitations. Journal of Property Investments & Finance, 39(5), 481–491. https://doi.org/10.1108/JPIF-07-2020-0086
Glumac, B., & Des Rosiers, F. (2021b). Towards a taxonomy for real estate and land automated valuation systems. Journal of Property Investments & Finance, 39(5), 450–463. https://doi.org/10.1108/JPIF-07-2020-0087
Helbich, M., Brunauer, W., Vaz, E., & Nijkamp, P. (2014). Spatial heterogeneity in hedonic house price models: the case of Austria. Urban Study, 51, 390–411. https://doi.org/10.1177/0042098013492234
Ho, W. K. O., Tang, B. S., & Wong, S. W. (2020). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48–70. https://doi.org/10.1080/09599916.2020.1832558
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 modelling to inform equitable housing policies. Land Use Policy, 82, 657–673. https://doi.org/10.1016/j.landusepol.2018.12.030
Ibeas, T., Cordera, R., dell’Olio, L., Coppola, P., & Dominquez, A. (2012). Modelling transport and real-estate values interactions in urban systems. Journal of Transport Geography, 24, 370–382. https://doi.org/10.1016/j.jtrangeo.2012.04.012
International Valuation Standards Committee. (2003). International Valuation Guidance Note No. 11: reviewing valuations. http://www.romacor.ro/legislatie/25-gn11.pdf
International Valuation Standards Council. (2017). International Valuation Standards. https://www.ivsc.org/standards/international-valuation-standards
Isakson, H. R. (1998). The review of real estate appraisals using multiple regression analysis. Journal of Real Estate Research, 15(1/2), 177–190. https://doi.org/10.1080/10835547.1998.12090922
Jahanshiri, E., Buyong, T., & Shariff, A. R. M. (2011). A review of property mass valuation models. Pertanika Journal of Science and Technology, 19(1), 23–30.
Jones, C. (2002). The definition of housing market area and strategic planning. Urban Studies, 39(3), 549–564. https://doi.org/10.1080/00420980220112829
Kaklauskas, A., Zavadskas, E. K., Bagdonavicius, A., Kelpsiene, L., Bardauskiene, D., & Kutut, V. (2010). Conceptual modelling of construction and real estate crisis with emphasis on comparative qualitative aspects descriptions. Transformations in Business and Economics, 9(1/19), 42–61.
Kauko, T., & d’Amato, M. (2008). Mass appraisal methods: an international perspective for property valuers. John Wiley & Sons. https://doi.org/10.1002/9781444301021
Kokinis-Graves, C. (2006). Use of the cost, income and sales-comparison approaches in the valuation of real estate. Journal of State Taxation, 23–32.
Kuburić, M., Tomić, H., & Mastelić Ivić, S. (2012). Use of multicriteria valuation of spatial units in a system of mass real estate valuation. KiG, 11(17), 58–74. https://hrcak.srce.hr/85966
Lai, P. Y. (2011). Analysis of the mass appraisal model by using artificial neural network in Kaohsiung city. Journal of Modern Accounting and Auditing, 7(10), 1080–1089.
Lamont, O., & Stein, J. (1999). Leverage and house price dynamics in US cities. RAND Journal of Economics, 30(3), 498–514.
Mathieson, K., & Dryer, B. J. (1993). Improving the effectiveness and efficiency of appraisal reviews: an information systems approach. Appraisal Journal, 61(3), 57–63.
Mayer, C. J., & Somerville, C. T. (2000). Land use regulation and new construction. Regional Science and Urban Economics, 30(6), 639–662. https://doi.org/10.1016/S0166-0462(00)00055-7
McCluskey, N., Davis, P., Haran, M., McCord, M., & McIlhatton, D. (2012). The potential of artificial neural networks in mass appraisal: the case revisited. Journal of Financial Management Property and Construction, 17(3), 274–292. https://doi.org/10.1108/13664381211274371
McCluskey, W. J., & Borst, R. (1997). An evaluation of MRA, comparable sales analysis and ANNs for the mass appraisal of residential properties in Northern Ireland. Assesment Journal, 4(1), 47–55.
McCulloch, C. E., & Searle, S. R. (2001). Generalized, linear, and mixed models. John Wiley & Sons. https://doi.org/10.1002/9780470057339.vag009
Mora-Esperanza, J. G. (2004). Artifical intelligence applied to real estate valuation: an example for the appraisal of Madrid. CATASTRO, 255–274.
Nguyen, N., & Cripps, A. (2001). Predicting housing value: a comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313–336. https://doi.org/10.1080/10835547.2001.12091068
Núñez Tabales, J. M., Caridad y Ocerin, J. M., & Rey Carmona, F. J. (2013). Artificial neural networks for predicting real estate prices. Revista de Metodos Cuantitativos para la Economia y la Empresa, 15(1), 29–44.
Peterson, S., & Flanagan, A. B. (2009). Neural network hedonic pricing models in mass real estate appraisal. Journal of Real Estate Research, 31(2), 147–164. https://doi.org/10.1080/10835547.2009.12091245
Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55. https://doi.org/10.1086/260169
Roulac, S. E. (1986). Real estate as a strategic resource (pp. 317–321). Chief Financial Officer International.
Sampathkumar, V., Santhi, M. H., & Vanjinathan, J. (2015). Evaluation of the trend of land price using regression and neural network models. Asian Journal of Scientific Research, 8(2), 182–194. https://doi.org/10.3923/ajsr.2015.182.194
Scheurwater, S. (2017). The future of valuations: the relevance of real estate valuations for institutional investors and banks – view from a European Expert Group. RICS.
Selim, H. (2009). Determinants of house prices in Turkey: hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2/2), 2843–2852. https://doi.org/10.1016/j.eswa.2008.01.044
Stevenson, S. (2004). New empirical evidence on heteroskedasticity in hedonic housing models. Journal of Housing Economics, 13(2), 136–153. https://doi.org/10.1016/j.jhe.2004.04.004
Tay, D. P. H., & Ho, D. K. H. (1992). Artificial intelligence and the mass appraisal of residential apartments. Journal of Property Valuation and Investment, 10(2), 525–540. https://doi.org/10.1108/14635789210031181
TEGoVA. (2020). European Valuation Standards (EVS). https://tegova.org/european-valuation-standards-evs
Valier, A., & Micelli, E. (2020). Automated models for value prediction: a critical review of the debate. Valori e Valutazioni, 24, 151–162.
Vascu, A. (2015). About valuation and valuation review [Despre evaluare și verificarea evaluării]. Hamangiu / IROVAL.
Vo, N., Shi, H., & Szajman, J. (2014). Optimisation to ANN inputs in automated property valuation model with Encog 3 and winGamma. Applied Mechanics and Materials, 462–463, 1081–1086. https://doi.org/10.4028/www.scientific.net/AMM.462-463.1081
Worzala, E., Lenk, M., & Silva, A. (1995). An exploration of neural networks and its application to real estate valuation. Journal of Real Estate Research, 10(2), 185–201. https://doi.org/10.1080/10835547.1995.12090782
Xie, X., & Hu, G. (2007). A comparison of Shanghai housing price index forecasting. In 3rd International Conference on Natural Computation (ICNC) (pp. 221–225), Haikou. https://doi.org/10.1109/ICNC.2007.14
Yacim, J. A., & Boshoff, D. G. B. (2018). Combiniang BP with PSO algorithms in weights optimisation and ANNs training for mass appraisal of properties. International Journal of Housing Markets and Analysis, 11(2), 290–314. https://doi.org/10.1108/IJHMA-02-2017-0021