The Use of Machine Learning in Real Estate Research
This research seeks to demonstrate how machine learning, a branch of artificial intelligence, is able to deliver more accurate pricing predictions, using the real estate market as an example. Utilizing 24,936 housing transaction records, this paper employs Extra Trees (ET), <i>k</i>–Near...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/12/4/740 |
| _version_ | 1797604739246981120 |
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| author | Lennon H. T. Choy Winky K. O. Ho |
| author_facet | Lennon H. T. Choy Winky K. O. Ho |
| author_sort | Lennon H. T. Choy |
| collection | DOAJ |
| description | This research seeks to demonstrate how machine learning, a branch of artificial intelligence, is able to deliver more accurate pricing predictions, using the real estate market as an example. Utilizing 24,936 housing transaction records, this paper employs Extra Trees (ET), <i>k</i>–Nearest Neighbors (KNN), and Random Forest (RF) to predict property prices and then compares their results with those of a hedonic price model. In particular, this paper uses a feature (property age x square footage) instead of property age in order to isolate the effect of land depreciation on property prices. Our results suggest that these three algorithms markedly outperform the traditional statistical techniques in terms of explanatory power and error minimization. Machine learning is expected to play an increasing role in shaping our future. However, it may raise questions about the privacy, fairness, and job displacement issues. It is therefore important to pay close attention to the ethical implications of machine learning and ensure that the technology is used responsibly and ethically. Researchers, legislators, and industry players must work together to create appropriate standards and legislation to govern the use of machine learning. |
| first_indexed | 2024-03-11T04:51:01Z |
| format | Article |
| id | doaj.art-697c10ff5e434ae58b3c0d0c31baebd5 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-11T04:51:01Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-697c10ff5e434ae58b3c0d0c31baebd52023-11-17T20:01:32ZengMDPI AGLand2073-445X2023-03-0112474010.3390/land12040740The Use of Machine Learning in Real Estate ResearchLennon H. T. Choy0Winky K. O. Ho1Department of Real Estate and Construction, University of Hong Kong, Hong Kong SAR, ChinaIndependent Researcher, Hong Kong SAR, ChinaThis research seeks to demonstrate how machine learning, a branch of artificial intelligence, is able to deliver more accurate pricing predictions, using the real estate market as an example. Utilizing 24,936 housing transaction records, this paper employs Extra Trees (ET), <i>k</i>–Nearest Neighbors (KNN), and Random Forest (RF) to predict property prices and then compares their results with those of a hedonic price model. In particular, this paper uses a feature (property age x square footage) instead of property age in order to isolate the effect of land depreciation on property prices. Our results suggest that these three algorithms markedly outperform the traditional statistical techniques in terms of explanatory power and error minimization. Machine learning is expected to play an increasing role in shaping our future. However, it may raise questions about the privacy, fairness, and job displacement issues. It is therefore important to pay close attention to the ethical implications of machine learning and ensure that the technology is used responsibly and ethically. Researchers, legislators, and industry players must work together to create appropriate standards and legislation to govern the use of machine learning.https://www.mdpi.com/2073-445X/12/4/740Extra Trees<i>k</i>–Nearest NeighborsRandom Forestproperty prices |
| spellingShingle | Lennon H. T. Choy Winky K. O. Ho The Use of Machine Learning in Real Estate Research Land Extra Trees <i>k</i>–Nearest Neighbors Random Forest property prices |
| title | The Use of Machine Learning in Real Estate Research |
| title_full | The Use of Machine Learning in Real Estate Research |
| title_fullStr | The Use of Machine Learning in Real Estate Research |
| title_full_unstemmed | The Use of Machine Learning in Real Estate Research |
| title_short | The Use of Machine Learning in Real Estate Research |
| title_sort | use of machine learning in real estate research |
| topic | Extra Trees <i>k</i>–Nearest Neighbors Random Forest property prices |
| url | https://www.mdpi.com/2073-445X/12/4/740 |
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