Alleviating surveyor bias in real estate: An application to vacancy and property prices
Abstract
Although vacancies in the real estate market have always been of key interest to both private and public stakeholders, measuring the impact of vacancy rates on property prices is challenging. This study attempts to quantify the extent of the influence of vacancy rates on the price decline of commercial properties. The dataset used in this study was collected and collated by valuation experts. We attempt to alleviate the bias inherent in the price estimates provided by the surveyors. A Bayesian multilevel estimation model was employed, and the results revealed that while some experts do not show deviations from the standard tendency of a peer group, others do diverge from this norm. Based on these findings, a bias-controlling price decline rate was derived. As many real estate statistics are produced based on survey data compiled by experts, the approach adopted in this study is expected to be applied to various real estate practices to mitigate the inherent and inevitable surveyor bias.
Keyword : surveyor bias, vacancy, property price, Bayesian estimation, multilevel model
How to Cite
Lee, C. (2024). Alleviating surveyor bias in real estate: An application to vacancy and property prices. International Journal of Strategic Property Management, 28(2), 93–100. https://doi.org/10.3846/ijspm.2024.21214
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Banuri, S., Dercon, S., & Gauri, V. (2019). Biased policy professionals. The World Bank Economic Review, 33(2), 310–327. https://doi.org/10.1093/wber/lhy033
Baum, A., Baum, C. M., Nunnington, N., & Mackmin, D. (2006). The income approach to property valuation. Estates Gazette. https://doi.org/10.4324/9780080937236
Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv. https://arxiv.org/pdf/1701.02434.pdf
Beullens, K., & Loosveldt, G. (2016). Interviewer effects in the European social survey. In Survey research methods (Vol. 10, No. 2, pp. 103–118). European Survey Research Association.
Cogbill, C. V. (2023). Surveyor and analyst biases in forest density estimation from United States public land surveys. Ecosphere, 14(8), Article e4647. https://doi.org/10.1002/ecs2.4647
Couch, C., & Cocks, M. (2013). Housing vacancy and the shrinking city: Trends and policies in the UK and the City of Liverpool. Housing Studies, 28(3), 499–519. https://doi.org/10.1080/02673037.2013.760029
Filewod, B., Kant, S., MacDonald, H., & McKenney, D. (2023). Decision biases and environmental attitudes among conservation professionals. Conservation Science and Practice, 5, Article e12921. https://doi.org/10.1111/csp2.12921
Gloudemans, R., & Almy, R. (2011). Fundamentals of mass appraisal. International Association of Assessing Officers, Kansas City, MO.
Griffin, B. N., & Wilson, I. G. (2010). Interviewer bias in medical student selection. Medical Journal of Australia, 193(6), 343–346. https://doi.org/10.5694/j.1326-5377.2010.tb03946.x
Hagen, D., & Hansen, J. (2010). Rental housing and the natural vacancy rate. Journal of Real Estate Research, 32(4), 413–434. https://doi.org/10.1080/10835547.2010.12091288
Hanberry, B. B., Yang, J., Kabrick, J. M., & He, H. S. (2012). Adjusting forest density estimates for surveyor bias in historical tree surveys. The American Midland Naturalist, 167(2), 285–306. https://doi.org/10.1674/0003-0031-167.2.285
Korean Statistical Information Service. (2022). National administration for houses and buildings. Daegeon City.
Kronenfeld, B. J. (2015). Validating the historical record: A relative distance test and correction formula for selection bias in presettlement land surveys. Ecography, 38(1), 41–53. https://doi.org/10.1111/ecog.00617
Langfeldt, L. (2004). Expert panels evaluating research: Decision-making and sources of bias. Research Evaluation, 13(1), 51–62. https://doi.org/10.3152/147154404781776536
Lee, C., & Ahn, J. (2020). Adjusting building assessed values and enhancing assessment accuracy. Korea Institute of Local Financing, Seoul, S. Korea.
Lerbs, O., & Teske, M. (2016). The house price-vacancy curve (Discussion Paper No. 16-082). ZEW-Centre for European Economic Research. https://doi.org/10.2139/ssrn.2884690
Malmendier, U., & Tate, G. (2008). Who makes acquisitions? CEO overconfidence and the market’s reaction. Journal of Financial Economics, 89(1), 20–43. https://doi.org/10.1016/j.jfineco.2007.07.002
Manville, M., & Kuhlmann, D. (2018). The social and fiscal consequences of urban decline: Evidence from large American cities, 1980–2010. Urban Affairs Review, 54(3), 451–489. https://doi.org/10.1177/1078087416675741
McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC. https://doi.org/10.1201/9781315372495
Morckel, V. C. (2013). Empty neighborhoods: Using constructs to predict the probability of housing abandonment. Housing Policy Debate, 23(3), 469–496. https://doi.org/10.1080/10511482.2013.788051
Moseley, A., & Thomann, E. (2021). A behavioural model of heuristics and biases in frontline policy implementation. Policy & Politics, 49(1), 49–67. https://doi.org/10.1332/030557320X15967973532891
Newman, G., Gu, D., Kim, J. H., & Li, W. (2016). Elasticity and urban vacancy: A longitudinal comparison of US cities. Cities, 58, 143–151. https://doi.org/10.1016/j.cities.2016.05.018
Parkhurst, J. (2017). The politics of evidence: From evidence-based policy to the good governance of evidence. Taylor & Francis. https://doi.org/10.4324/9781315675008
Quas, J. A., Malloy, L. C., Melinder, A., Goodman, G. S., D’Mello, M., & Schaaf, J. (2007). Developmental differences in the effects of repeated interviews and interviewer bias on young children’s event memory and false reports. Developmental Psychology, 43(4), Article 823. https://doi.org/10.1037/0012-1649.43.4.823
Reyes-García, V., Vadez, V., Godoy, R., Byron, E., Huanca, T., & Leonard, W. R. (2005). Interviewer bias: Lessons from panel and cross-sectional surveys from a native Amazonian society (Tsimane’ Amazonian Panel Study Working Paper # 15, pp. 1–26). https://heller.brandeis.edu/sustainable-international-development/tsimane/wp/TAPS-WP-15-BIAS-Nov-2005.pdf
Stone, D. A. (2022). Policy paradox: The art of political decision making. WW Norton & Company.
Sukhera, J., Watling, C. J., & Gonzalez, C. M. (2020). Implicit bias in health professions: From recognition to transformation. Academic Medicine, 95(5), 717–723. https://doi.org/10.1097/ACM.0000000000003173
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Weber, M. (1946). Bureaucracy and law. In H. H. Gerth & C. Wright Mills (Eds.), From Max Weber: Essays in sociology (pp. 216–220). Oxford University Press.
West, B. T., & Blom, A. G. (2017). Explaining interviewer effects: A research synthesis. Journal of Survey Statistics and Methodology, 5(2), 175–211.
Williams, M. A., & Baker, W. L. (2010). Bias and error in using survey records for ponderosa pine landscape restoration. Journal of Biogeography, 37(4), 707–721. https://doi.org/10.1111/j.1365-2699.2009.02257.x
Wynder, E. L. (1994). Investigator bias and interviewer bias: The problem of reporting systematic error in epidemiology. Journal of Clinical Epidemiology, 47(8), 825–827. https://doi.org/10.1016/0895-4356(94)90184-8
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R (Vol. 574). Springer. https://doi.org/10.1007/978-0-387-87458-6
Baum, A., Baum, C. M., Nunnington, N., & Mackmin, D. (2006). The income approach to property valuation. Estates Gazette. https://doi.org/10.4324/9780080937236
Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv. https://arxiv.org/pdf/1701.02434.pdf
Beullens, K., & Loosveldt, G. (2016). Interviewer effects in the European social survey. In Survey research methods (Vol. 10, No. 2, pp. 103–118). European Survey Research Association.
Cogbill, C. V. (2023). Surveyor and analyst biases in forest density estimation from United States public land surveys. Ecosphere, 14(8), Article e4647. https://doi.org/10.1002/ecs2.4647
Couch, C., & Cocks, M. (2013). Housing vacancy and the shrinking city: Trends and policies in the UK and the City of Liverpool. Housing Studies, 28(3), 499–519. https://doi.org/10.1080/02673037.2013.760029
Filewod, B., Kant, S., MacDonald, H., & McKenney, D. (2023). Decision biases and environmental attitudes among conservation professionals. Conservation Science and Practice, 5, Article e12921. https://doi.org/10.1111/csp2.12921
Gloudemans, R., & Almy, R. (2011). Fundamentals of mass appraisal. International Association of Assessing Officers, Kansas City, MO.
Griffin, B. N., & Wilson, I. G. (2010). Interviewer bias in medical student selection. Medical Journal of Australia, 193(6), 343–346. https://doi.org/10.5694/j.1326-5377.2010.tb03946.x
Hagen, D., & Hansen, J. (2010). Rental housing and the natural vacancy rate. Journal of Real Estate Research, 32(4), 413–434. https://doi.org/10.1080/10835547.2010.12091288
Hanberry, B. B., Yang, J., Kabrick, J. M., & He, H. S. (2012). Adjusting forest density estimates for surveyor bias in historical tree surveys. The American Midland Naturalist, 167(2), 285–306. https://doi.org/10.1674/0003-0031-167.2.285
Korean Statistical Information Service. (2022). National administration for houses and buildings. Daegeon City.
Kronenfeld, B. J. (2015). Validating the historical record: A relative distance test and correction formula for selection bias in presettlement land surveys. Ecography, 38(1), 41–53. https://doi.org/10.1111/ecog.00617
Langfeldt, L. (2004). Expert panels evaluating research: Decision-making and sources of bias. Research Evaluation, 13(1), 51–62. https://doi.org/10.3152/147154404781776536
Lee, C., & Ahn, J. (2020). Adjusting building assessed values and enhancing assessment accuracy. Korea Institute of Local Financing, Seoul, S. Korea.
Lerbs, O., & Teske, M. (2016). The house price-vacancy curve (Discussion Paper No. 16-082). ZEW-Centre for European Economic Research. https://doi.org/10.2139/ssrn.2884690
Malmendier, U., & Tate, G. (2008). Who makes acquisitions? CEO overconfidence and the market’s reaction. Journal of Financial Economics, 89(1), 20–43. https://doi.org/10.1016/j.jfineco.2007.07.002
Manville, M., & Kuhlmann, D. (2018). The social and fiscal consequences of urban decline: Evidence from large American cities, 1980–2010. Urban Affairs Review, 54(3), 451–489. https://doi.org/10.1177/1078087416675741
McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC. https://doi.org/10.1201/9781315372495
Morckel, V. C. (2013). Empty neighborhoods: Using constructs to predict the probability of housing abandonment. Housing Policy Debate, 23(3), 469–496. https://doi.org/10.1080/10511482.2013.788051
Moseley, A., & Thomann, E. (2021). A behavioural model of heuristics and biases in frontline policy implementation. Policy & Politics, 49(1), 49–67. https://doi.org/10.1332/030557320X15967973532891
Newman, G., Gu, D., Kim, J. H., & Li, W. (2016). Elasticity and urban vacancy: A longitudinal comparison of US cities. Cities, 58, 143–151. https://doi.org/10.1016/j.cities.2016.05.018
Parkhurst, J. (2017). The politics of evidence: From evidence-based policy to the good governance of evidence. Taylor & Francis. https://doi.org/10.4324/9781315675008
Quas, J. A., Malloy, L. C., Melinder, A., Goodman, G. S., D’Mello, M., & Schaaf, J. (2007). Developmental differences in the effects of repeated interviews and interviewer bias on young children’s event memory and false reports. Developmental Psychology, 43(4), Article 823. https://doi.org/10.1037/0012-1649.43.4.823
Reyes-García, V., Vadez, V., Godoy, R., Byron, E., Huanca, T., & Leonard, W. R. (2005). Interviewer bias: Lessons from panel and cross-sectional surveys from a native Amazonian society (Tsimane’ Amazonian Panel Study Working Paper # 15, pp. 1–26). https://heller.brandeis.edu/sustainable-international-development/tsimane/wp/TAPS-WP-15-BIAS-Nov-2005.pdf
Stone, D. A. (2022). Policy paradox: The art of political decision making. WW Norton & Company.
Sukhera, J., Watling, C. J., & Gonzalez, C. M. (2020). Implicit bias in health professions: From recognition to transformation. Academic Medicine, 95(5), 717–723. https://doi.org/10.1097/ACM.0000000000003173
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Weber, M. (1946). Bureaucracy and law. In H. H. Gerth & C. Wright Mills (Eds.), From Max Weber: Essays in sociology (pp. 216–220). Oxford University Press.
West, B. T., & Blom, A. G. (2017). Explaining interviewer effects: A research synthesis. Journal of Survey Statistics and Methodology, 5(2), 175–211.
Williams, M. A., & Baker, W. L. (2010). Bias and error in using survey records for ponderosa pine landscape restoration. Journal of Biogeography, 37(4), 707–721. https://doi.org/10.1111/j.1365-2699.2009.02257.x
Wynder, E. L. (1994). Investigator bias and interviewer bias: The problem of reporting systematic error in epidemiology. Journal of Clinical Epidemiology, 47(8), 825–827. https://doi.org/10.1016/0895-4356(94)90184-8
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G. M. (2009). Mixed effects models and extensions in ecology with R (Vol. 574). Springer. https://doi.org/10.1007/978-0-387-87458-6