%0 Journal Article %1 birkeland2021predictability %A Birkeland, Kristoffer B. %A D’Silva, Allan D. %A Füss, Roland %A Oust, Are %D 2021 %J International Real Estate Review %K 2021 FDZ_GML SILC SILC_contra_zt SILC_input2021 article bat english jak text zt_proved %N 2 %P 139-183 %T The Predictability of House Prices: “Human Against Machine” %U https://www.gssinst.org/irer/2021/07/16/the-predictability-of-house-prices_human-against-machine/ %V 24 %X We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.

@article{birkeland2021predictability, abstract = {We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.}, added-at = {2021-09-07T10:20:15.000+0200}, author = {Birkeland, Kristoffer B. and D’Silva, Allan D. and Füss, Roland and Oust, Are}, biburl = {https://www.bibsonomy.org/bibtex/26f087415824dcdb165c371a316a079c7/gesis_survey21}, description = {study data-doi gesis_study_no}, interhash = {1d8217a84a71a46278a56913d62c48db}, intrahash = {6f087415824dcdb165c371a316a079c7}, journal = {International Real Estate Review}, keywords = {2021 FDZ_GML SILC SILC_contra_zt SILC_input2021 article bat english jak text zt_proved}, language = {english}, note = {(SILC)}, number = 2, pages = {139-183}, timestamp = {2022-01-11T11:57:57.000+0100}, title = {The Predictability of House Prices: “Human Against Machine”}, url = {https://www.gssinst.org/irer/2021/07/16/the-predictability-of-house-prices_human-against-machine/}, volume = 24, year = 2021 }