Identifying Real Estate Opportunities Using Machine Learning
The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/8/11/2321 |
| _version_ | 1818278043957854208 |
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| author | Alejandro Baldominos Iván Blanco Antonio José Moreno Rubén Iturrarte Óscar Bernárdez Carlos Afonso |
| author_facet | Alejandro Baldominos Iván Blanco Antonio José Moreno Rubén Iturrarte Óscar Bernárdez Carlos Afonso |
| author_sort | Alejandro Baldominos |
| collection | DOAJ |
| description | The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, <i>k</i>-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them. |
| first_indexed | 2024-12-12T23:11:10Z |
| format | Article |
| id | doaj.art-3e442f2d51064083a7bef6e6650492f8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T23:11:10Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3e442f2d51064083a7bef6e6650492f82022-12-22T00:08:34ZengMDPI AGApplied Sciences2076-34172018-11-01811232110.3390/app8112321app8112321Identifying Real Estate Opportunities Using Machine LearningAlejandro Baldominos0Iván Blanco1Antonio José Moreno2Rubén Iturrarte3Óscar Bernárdez4Carlos Afonso5Computer Science Department, Universidad Carlos III de Madrid, 28911 Leganés, SpainFinance Department, Colegio Universitario de Estudios Financieros, 28040 Madrid, SpainArtificial Intelligence Group, Rentier Token, 28050 Madrid, SpainArtificial Intelligence Group, Rentier Token, 28050 Madrid, SpainArtificial Intelligence Group, Rentier Token, 28050 Madrid, SpainArtificial Intelligence Group, Rentier Token, 28050 Madrid, SpainThe real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, <i>k</i>-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.https://www.mdpi.com/2076-3417/8/11/2321real estateappraisalinvestmentmachine learningartificial intelligence |
| spellingShingle | Alejandro Baldominos Iván Blanco Antonio José Moreno Rubén Iturrarte Óscar Bernárdez Carlos Afonso Identifying Real Estate Opportunities Using Machine Learning Applied Sciences real estate appraisal investment machine learning artificial intelligence |
| title | Identifying Real Estate Opportunities Using Machine Learning |
| title_full | Identifying Real Estate Opportunities Using Machine Learning |
| title_fullStr | Identifying Real Estate Opportunities Using Machine Learning |
| title_full_unstemmed | Identifying Real Estate Opportunities Using Machine Learning |
| title_short | Identifying Real Estate Opportunities Using Machine Learning |
| title_sort | identifying real estate opportunities using machine learning |
| topic | real estate appraisal investment machine learning artificial intelligence |
| url | https://www.mdpi.com/2076-3417/8/11/2321 |
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