Identifying Real Estate Opportunities Using Machine Learning

Abstract

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 attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, $k$-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

Who performs better? AVMs vs hedonic models

“…Almost all the conclusions obtained by the authors confirm that the econometric approach has good inferential capacities and poor predictive capacities. Machine learning models, vice versa (Baldominos et al, 2018;P erez-Rave et al, 2019).…”

Volume 19 : 2021 Issue 2

“…According to Baldominos and Blanco (2018), through the use of machine learning, the hidden value in data sources can be analysed to derive actionable insights from the data. The application of machine learning can also help identify more opportunities in the real estate market.…”

Machine learning building price prediction with green building determinant

“…This will only happen if the valuer considers the historical factors in predicting the price of the GB. ML is seen to have the potential in considering those factors and problems [14].…”

“…The common ML modelling techniques that are already being implemented in real estate problems are Linear Regression [21][22][23], Decision Tree [24][25][26][27], Random Forest [21,[28][29], Ridge Regression [30] and Lasso Regression [24,31]. The function of all these algorithms is to predict the real estate dataset and the researchers test all these algorithms in order to predict the green building prices.…”

“…Lack of data means lack of support for the valuable contributions of green attributes, which is supposed to be the factor influencing the GB price. Indeed, the real estate market is exposed to many price fluctuations due to existing correlations with many variables and some of which are beyond our control or perhaps unknown [14].…”