Does machine learning prediction dampen the information asymmetry for non-local investors?
Abstract
In this study, we examine the prediction accuracy of machine learning methods to estimate commercial real estate transaction prices. Using machine learning methods, including Random Forest (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Deep Neural Networks (DNN), we estimate the commercial real estate transaction price by comparing relative prediction accuracy. Data consist of 19,640 transaction-based office properties provided by Costar corresponding to the 2004–2017 period for 10 major U.S. CMSA (Consolidated Metropolitan Statistical Area). We conduct each machine learning method and compare the performance to identify a critical determinant model for each office market. Furthermore, we depict a partial dependence plot (PD) to verify the impact of research variables on predicted commercial office property value. In general, we expect that results from machine learning will provide a set of critical determinants to commercial office price with more predictive power overcoming the limitation of the traditional valuation model. The result for 10 CMSA will provide critical implications for the out-of-state investors to understand regional commercial real estate market.
Keyword : machine learning, office price, commercial real estate, prediction accuracy, information asymmetry, non-local investors
How to Cite
Jung, J., Kim, J., & Jin, C. (2022). Does machine learning prediction dampen the information asymmetry for non-local investors?. International Journal of Strategic Property Management, 26(5), 345–361. https://doi.org/10.3846/ijspm.2022.17590
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Routledge. https://doi.org/10.1201/9781315139470
Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the performance of random forest versus multiple regression for predicting prices of the apartments. ISPRS International Journal of Geo-Information, 7(5), 168. https://doi.org/10.3390/ijgi7050168
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Conway, J. J. E. (2018). Artificial intelligence and machine learning: current applications in real estate. https://dspace.mit.edu/handle/1721.1/120609
Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Cowden, C., Fabozzi, F. J., & Nazemi, A. (2019). Default prediction of commercial real estate properties using machine learning techniques. The Journal of Portfolio Management, 45(7), 55–67. https://doi.org/10.3905/jpm.2019.1.104
Egan, M. (2019, February 17). How elite investors use artificial intelligence and machine learning to gain an edge. CNN Business. https://edition.cnn.com/2019/02/17/investing/artificial-intelligence-investors-machine-learning
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189–1232. https://doi.org/10.1214/aos/1013203451
Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
Gallimore, P., & Wolverton, M. (2000). The objective in valuation: a study of the influence of client feedback. Journal of Property Research, 17(1), 47–57. https://doi.org/10.1080/095999100368010
Geltner, D., MacGregor, B. D., & Schwann, G. M. (2003). Appraisal smoothing and price discovery in real estate markets. Urban Studies, 40(5–6), 1047–1064. https://doi.org/10.1080/0042098032000074317
Gupta, R., Marfatia, H. A., Pierdzioch, C., & Salisu, A. A. (2022). Machine learning predictions of housing market synchronization across US states: the role of uncertainty. The Journal of Real Estate Finance and Economics, 64, 523–545. https://doi.org/10.1007/s11146-020-09813-1
Han, L., & Hong, S. H. (2016). Understanding in‐house transactions in the real estate brokerage industry. The RAND Journal of Economics, 47(4), 1057–1086. https://doi.org/10.1111/1756-2171.12163
Hansz, J. A., & Diaz III, J. (2001). Valuation bias in commercial appraisal: a transaction price feedback experiment. Real Estate Economics, 29(4), 553–565. https://doi.org/10.1111/1080-8620.00022
Hill, R. J. (2013). Hedonic price indexes for residential housing: a survey, evaluation and taxonomy. Journal of Economic Surveys, 27(5), 879–914.
Ho, W., Tang, B., & Wong, S. W. (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48–70. https://doi.org/10.1080/09599916.2020.1832558
Hochreiter, S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), 107–116. https://doi.org/10.1142/S0218488598000094
International Association of Assessing Officers. (2013). Standard on mass appraisal of real property. https://www.iaao.org/media/standards/StandardOnMassAppraisal.pdf
Izmailov, R., Vapnik, V., & Vashist, A. (2013). Multi-dimensional splines with infinite number of knots as SVM kernels. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). IEEE. https://doi.org/10.1109/IJCNN.2013.6706860
Kandlbinder, K. (2018). The role of information in real estate markets [Doctoral dissertation, Universität Regensburg]. https://epub.uni-regensburg.de/37492/1/00%20Dissertation_Pflichtexemplare.pdf
Kok, N., Koponen, E. L., & Martínez-Barbosa, C. A. (2017). Big data in real estate? From manual appraisal to automated valuation. The Journal of Portfolio Management, 43(6), 202–211. https://doi.org/10.3905/jpm.2017.43.6.202
Kotu, V., & Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-801460-8.00013-6
Lam, K. C., Yu, C. Y., & Lam, C. K. (2009). Support vector machine and entropy-based decision support system for property valuation. Journal of Property Research, 26(3), 213–233. https://doi.org/10.1080/09599911003669674
Ling, D. C., Naranjo, A., & Scheick, B. (2018). Geographic portfolio allocations, property selection and performance attribution in public and private real estate markets. Real Estate Economics, 46(2), 404–448. https://doi.org/10.1111/1540-6229.12184
Liu, Y., Gallimore, P., & Wiley, J. A. (2015). Non-local office investors: anchored by their markets and impaired by their distance. The Journal of Real Estate Finance and Economics, 50(1), 129–149. https://doi.org/10.1007/s11146-013-9446-8
McMillen, D. P., & Redfearn, C. L. (2010). Estimation and hypothesis testing for nonparametric hedonic house price functions. Journal of Regional Science, 50(3), 712–733. https://doi.org/10.1111/j.1467-9787.2010.00664.x
Meese, R. A., & Wallace, N. E. (1991). Nonparametric estimation of dynamic hedonic price models and the construction of residential housing price indices. Real Estate Economics, 19(3), 308–332. https://doi.org/10.1111/1540-6229.00555
Moghaddam, D. D., Rahmati, O., Panahi, M., Tiefenbacher, J., Darabi, H., Haghizadeh, A., Haghighi, A. T., Nalivang, O. A., & Bui, D. T. (2020). The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. Catena, 187, 104421. https://doi.org/10.1016/j.catena.2019.104421
Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
Nghiep, N., & Al, C. (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
Parmeter, C. F., Henderson, D. J., & Kumbhakar, S. C. (2007). Nonparametric estimation of a hedonic price function. Journal of Applied Econometrics, 22(3), 695–699. https://doi.org/10.1002/jae.929
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/10.1080/09599916.2019.1587489
Peterson, S., & Flanagan, A. (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
Probst, P., Wright, M. N., & Boulesteix, A.-L. (2019). Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3), e1301. https://doi.org/10.1002/widm.1301
Rafiei, M. H., & Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2), 04015066. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001047
Rice, L., Wong, E., & Kolter, Z. (2020). Overfitting in adversarially robust deep learning. In International Conference on Machine Learning (pp. 8093–8104). PMLR.
Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227. https://doi.org/10.1007/BF00116037
Schapire, R., & Freund, Y. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In Second European Conference on Computational Learning Theory (pp. 23–37). Springer. https://doi.org/10.1007/3-540-59119-2_166
Shen, L., & Ross, S. (2021). Information value of property description: a machine learning approach. Journal of Urban Economics, 121, 103299. https://doi.org/10.1016/j.jue.2020.103299
Shi, L., & Tapia, C. (2016). The disciplining effect of concern for referrals: evidence from real estate agents. Real Estate Economics, 44(2), 411–461. https://doi.org/10.1111/1540-6229.12102
Simlai, P. E. (2021). Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data. Journal of Property Research, 38(4), 305–336. https://doi.org/10.1080/09599916.2021.1890187
Sun, X., Ren, X., Ma, S., & Wang, H. (2017). meProp: sparsified back propagation for accelerated deep learning with reduced overfitting. In International Conference on Machine Learning (pp. 3299–3308). PMLR.
Telgarsky, M. (2013). Margins, shrinkage, and boosting. In International Conference on Machine Learning (pp. 307–315). PMLR.
Turnbull, G. K., & Sirmans, C. F. (1993). Information, search, and house prices. Regional Science and Urban Economics, 23(4), 545–557. https://doi.org/10.1016/0166-0462(93)90046-H
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019). Machine learning algorithm validation with a limited sample size. PloS ONE, 14(11), e0224365. https://doi.org/10.1371/journal.pone.0224365
Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.
Wong, S. K., Yiu, C. Y., & Chau, K. W. (2012). Liquidity and information asymmetry in the real estate market. The Journal of Real Estate Finance and Economics, 45(1), 49–62. https://doi.org/10.1007/s11146-011-9326-z
Xu, H., & Gade, A. (2017). Smart real estate assessments using structured deep neural networks. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (pp. 1–7). IEEE. https://doi.org/10.1109/UIC-ATC.2017.8397560
Yao, Y., Zhang, J., Qian, C., Wang, Y., Ren, S., Yuan, Z., & Guan, Q. (2021). Delineating urban job-housing patterns at a parcel scale with street view imagery. International Journal of Geographical Information Science, 35(10), 1927–1950. https://doi.org/10.1080/13658816.2021.1895170
Yilmazer, S., & Kocaman, S. (2020). A mass appraisal assessment study using machine learning based on multiple regression and random forest. Land Use Policy, 99, 104889. https://doi.org/10.1016/j.landusepol.2020.104889
You, Q., Pang, R., Cao, L., & Luo, J. (2017). Image-based appraisal of real estate properties. IEEE Transactions on Multimedia, 19(12), 2751–2759. https://doi.org/10.1109/TMM.2017.2710804
Yu, L., Jiao, C., Xin, H., Wang, Y., & Wang, K. (2018). Prediction on housing price based on deep learning. International Journal of Computer and Information Engineering, 12(2), 90–99.
Zhou, X., Gibler, K., & Zahirovic-Herbert, V. (2015). Asymmetric buyer information influence on price in a homogeneous housing market. Urban Studies, 52(5), 891–905. https://doi.org/10.1177/0042098014529464
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Routledge. https://doi.org/10.1201/9781315139470
Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the performance of random forest versus multiple regression for predicting prices of the apartments. ISPRS International Journal of Geo-Information, 7(5), 168. https://doi.org/10.3390/ijgi7050168
Colwell, P., Munneke, H., & Trefzger, J. (1998) Chicago’s office market: price indices, location and time. Real Estate Economics, 26(1), 83–106. https://doi.org/10.1111/1540-6229.00739
Conway, J. J. E. (2018). Artificial intelligence and machine learning: current applications in real estate. https://dspace.mit.edu/handle/1721.1/120609
Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Cowden, C., Fabozzi, F. J., & Nazemi, A. (2019). Default prediction of commercial real estate properties using machine learning techniques. The Journal of Portfolio Management, 45(7), 55–67. https://doi.org/10.3905/jpm.2019.1.104
Egan, M. (2019, February 17). How elite investors use artificial intelligence and machine learning to gain an edge. CNN Business. https://edition.cnn.com/2019/02/17/investing/artificial-intelligence-investors-machine-learning
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189–1232. https://doi.org/10.1214/aos/1013203451
Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
Gallimore, P., & Wolverton, M. (2000). The objective in valuation: a study of the influence of client feedback. Journal of Property Research, 17(1), 47–57. https://doi.org/10.1080/095999100368010
Geltner, D., MacGregor, B. D., & Schwann, G. M. (2003). Appraisal smoothing and price discovery in real estate markets. Urban Studies, 40(5–6), 1047–1064. https://doi.org/10.1080/0042098032000074317
Gupta, R., Marfatia, H. A., Pierdzioch, C., & Salisu, A. A. (2022). Machine learning predictions of housing market synchronization across US states: the role of uncertainty. The Journal of Real Estate Finance and Economics, 64, 523–545. https://doi.org/10.1007/s11146-020-09813-1
Han, L., & Hong, S. H. (2016). Understanding in‐house transactions in the real estate brokerage industry. The RAND Journal of Economics, 47(4), 1057–1086. https://doi.org/10.1111/1756-2171.12163
Hansz, J. A., & Diaz III, J. (2001). Valuation bias in commercial appraisal: a transaction price feedback experiment. Real Estate Economics, 29(4), 553–565. https://doi.org/10.1111/1080-8620.00022
Hill, R. J. (2013). Hedonic price indexes for residential housing: a survey, evaluation and taxonomy. Journal of Economic Surveys, 27(5), 879–914.
Ho, W., Tang, B., & Wong, S. W. (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48–70. https://doi.org/10.1080/09599916.2020.1832558
Hochreiter, S. (1998). The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02), 107–116. https://doi.org/10.1142/S0218488598000094
International Association of Assessing Officers. (2013). Standard on mass appraisal of real property. https://www.iaao.org/media/standards/StandardOnMassAppraisal.pdf
Izmailov, R., Vapnik, V., & Vashist, A. (2013). Multi-dimensional splines with infinite number of knots as SVM kernels. In The 2013 International Joint Conference on Neural Networks (IJCNN) (pp. 1–7). IEEE. https://doi.org/10.1109/IJCNN.2013.6706860
Kandlbinder, K. (2018). The role of information in real estate markets [Doctoral dissertation, Universität Regensburg]. https://epub.uni-regensburg.de/37492/1/00%20Dissertation_Pflichtexemplare.pdf
Kok, N., Koponen, E. L., & Martínez-Barbosa, C. A. (2017). Big data in real estate? From manual appraisal to automated valuation. The Journal of Portfolio Management, 43(6), 202–211. https://doi.org/10.3905/jpm.2017.43.6.202
Kotu, V., & Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer. Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-801460-8.00013-6
Lam, K. C., Yu, C. Y., & Lam, C. K. (2009). Support vector machine and entropy-based decision support system for property valuation. Journal of Property Research, 26(3), 213–233. https://doi.org/10.1080/09599911003669674
Ling, D. C., Naranjo, A., & Scheick, B. (2018). Geographic portfolio allocations, property selection and performance attribution in public and private real estate markets. Real Estate Economics, 46(2), 404–448. https://doi.org/10.1111/1540-6229.12184
Liu, Y., Gallimore, P., & Wiley, J. A. (2015). Non-local office investors: anchored by their markets and impaired by their distance. The Journal of Real Estate Finance and Economics, 50(1), 129–149. https://doi.org/10.1007/s11146-013-9446-8
McMillen, D. P., & Redfearn, C. L. (2010). Estimation and hypothesis testing for nonparametric hedonic house price functions. Journal of Regional Science, 50(3), 712–733. https://doi.org/10.1111/j.1467-9787.2010.00664.x
Meese, R. A., & Wallace, N. E. (1991). Nonparametric estimation of dynamic hedonic price models and the construction of residential housing price indices. Real Estate Economics, 19(3), 308–332. https://doi.org/10.1111/1540-6229.00555
Moghaddam, D. D., Rahmati, O., Panahi, M., Tiefenbacher, J., Darabi, H., Haghizadeh, A., Haghighi, A. T., Nalivang, O. A., & Bui, D. T. (2020). The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. Catena, 187, 104421. https://doi.org/10.1016/j.catena.2019.104421
Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106. https://doi.org/10.1257/jep.31.2.87
Nghiep, N., & Al, C. (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
Parmeter, C. F., Henderson, D. J., & Kumbhakar, S. C. (2007). Nonparametric estimation of a hedonic price function. Journal of Applied Econometrics, 22(3), 695–699. https://doi.org/10.1002/jae.929
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/10.1080/09599916.2019.1587489
Peterson, S., & Flanagan, A. (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
Probst, P., Wright, M. N., & Boulesteix, A.-L. (2019). Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3), e1301. https://doi.org/10.1002/widm.1301
Rafiei, M. H., & Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2), 04015066. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001047
Rice, L., Wong, E., & Kolter, Z. (2020). Overfitting in adversarially robust deep learning. In International Conference on Machine Learning (pp. 8093–8104). PMLR.
Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227. https://doi.org/10.1007/BF00116037
Schapire, R., & Freund, Y. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In Second European Conference on Computational Learning Theory (pp. 23–37). Springer. https://doi.org/10.1007/3-540-59119-2_166
Shen, L., & Ross, S. (2021). Information value of property description: a machine learning approach. Journal of Urban Economics, 121, 103299. https://doi.org/10.1016/j.jue.2020.103299
Shi, L., & Tapia, C. (2016). The disciplining effect of concern for referrals: evidence from real estate agents. Real Estate Economics, 44(2), 411–461. https://doi.org/10.1111/1540-6229.12102
Simlai, P. E. (2021). Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data. Journal of Property Research, 38(4), 305–336. https://doi.org/10.1080/09599916.2021.1890187
Sun, X., Ren, X., Ma, S., & Wang, H. (2017). meProp: sparsified back propagation for accelerated deep learning with reduced overfitting. In International Conference on Machine Learning (pp. 3299–3308). PMLR.
Telgarsky, M. (2013). Margins, shrinkage, and boosting. In International Conference on Machine Learning (pp. 307–315). PMLR.
Turnbull, G. K., & Sirmans, C. F. (1993). Information, search, and house prices. Regional Science and Urban Economics, 23(4), 545–557. https://doi.org/10.1016/0166-0462(93)90046-H
Vabalas, A., Gowen, E., Poliakoff, E., & Casson, A. J. (2019). Machine learning algorithm validation with a limited sample size. PloS ONE, 14(11), e0224365. https://doi.org/10.1371/journal.pone.0224365
Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.
Wong, S. K., Yiu, C. Y., & Chau, K. W. (2012). Liquidity and information asymmetry in the real estate market. The Journal of Real Estate Finance and Economics, 45(1), 49–62. https://doi.org/10.1007/s11146-011-9326-z
Xu, H., & Gade, A. (2017). Smart real estate assessments using structured deep neural networks. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (pp. 1–7). IEEE. https://doi.org/10.1109/UIC-ATC.2017.8397560
Yao, Y., Zhang, J., Qian, C., Wang, Y., Ren, S., Yuan, Z., & Guan, Q. (2021). Delineating urban job-housing patterns at a parcel scale with street view imagery. International Journal of Geographical Information Science, 35(10), 1927–1950. https://doi.org/10.1080/13658816.2021.1895170
Yilmazer, S., & Kocaman, S. (2020). A mass appraisal assessment study using machine learning based on multiple regression and random forest. Land Use Policy, 99, 104889. https://doi.org/10.1016/j.landusepol.2020.104889
You, Q., Pang, R., Cao, L., & Luo, J. (2017). Image-based appraisal of real estate properties. IEEE Transactions on Multimedia, 19(12), 2751–2759. https://doi.org/10.1109/TMM.2017.2710804
Yu, L., Jiao, C., Xin, H., Wang, Y., & Wang, K. (2018). Prediction on housing price based on deep learning. International Journal of Computer and Information Engineering, 12(2), 90–99.
Zhou, X., Gibler, K., & Zahirovic-Herbert, V. (2015). Asymmetric buyer information influence on price in a homogeneous housing market. Urban Studies, 52(5), 891–905. https://doi.org/10.1177/0042098014529464