# Identifying Real Estate Opportunities Using Machine Learning
## 2 State of the Art
For constructing the model, eight variables were considered: land area, built-up area, number of bedrooms, number of bathrooms, building age, repair condition, quality of furniture and location, along with the price in Malaysian ringgits (MYR). Those features which were categorial were first converted into numeric attributes to be manageable by the model. The authors reported a mean absolute error around 183,000 for FLSR, smaller than ANFIS and artificial neural networks. Finally, in 2017, [PERSON] et al. [10] introduced an approach where fuzzy logic was used to evaluate the situations of a real estate market with imprecise information, showing a case study which focused on the purchase of one office building. Interestingly, [PERSON] et al. state that, while there is a wide theoretical background regarding real estate investment, empirical support for such theory is scarce in the literature. A work by [PERSON] et al. [11] presents an innovative approach where the focus is placed not in property appraisal but in decision making at the time of starting a new construction, i.e., whether \"to build or not to build\", in the words of the authors. Authors describe the use of a deep belief restricted Boltzmann machine for learning a model from 350 condos (3-9 stories) built in Tehran (Iran) between 1993 and 2008. From these condos, a total of 26 attributes are used to train the model. Seven attributes refer to physical and financial properties of the constructions, such as the ZIP code, the total floor area and the lot area, the estimated construction cost per square meter, the duration of construction and the price of the unit at the beginning of the project. The authors also retrieved additional economic variables, which included regulation in the area, prices index, financial status in the construction area, currency exchange rate, population and demographics, etc. When evaluating their model over a test set of another 10 condos, they obtain a test error as low as 3.6%, a number better than that attained using neural networks trained with backpropagation. Finally, some authors have relied on the use of machine learning techniques for estimating or predicting the price of individual real estate assets. It is the case of [PERSON] and [PERSON] [12], who have analyzed housing data of 5359 townhouses in Fairfax County, Virginia, combined from various databases, from 2004 and 2007. These assets had 76 attributes, from which 28 were eventually selected after filtering using a \(t\)-test and logistic regression. Sixteen of these features are physical features, referring to the number of bedrooms and bathrooms, number of fireplaces, total area, cooling and heating systems, parking type, etc. In addition, three variables refer to the ratings of elementary, middle and high schools in the area, and other eight refer to the mortgage contract rate, location and construction and sale date. The last attribute is the class, which the authors have converted in binary: either the closing price is larger than the listing price or the other way around. Therefore, the problem can be seen as a classification to decide whether an investment is worthy or not instead of a regression problem. For performing classification, authors compare different algorithms: decision trees (C4.5), RIPPER, Naive Bayes and AdaBoost. Best results are achieved using RIPPER (repeated incremental pruning to produce error reduction), a propositional rule learner, with an average error of about 25%. The authors do not provide more specific metrics such as F1 score which allows for determining the model goodness regardless of the class distribution. Another work was presented by [PERSON] et al. [13] in 2016, where the focus is put on appraisal based on 148 sales of residential property in a city in the Campania region, Italy. Considered variables are the age of the property, date of sale, internal area, balconies area, connected area, number of services, number of views, maintenance status and floor level.