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 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, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.Comment: 24 pages, 13 figures, 5 table

Data Mining Techniques for Predicting Real Estate Trends

A wide variety of businesses and government agencies support the U.S. real estate market. Examples would include sales agents, national lenders, local credit unions, private mortgage and title insurers, and government sponsored entities (Freddie Mac and Fannie Mae), to name a few. The financial performance and overall success of these organizations depends in large part on the health of the overall real estate market. According to the National Association of Home Builders (NAHB), the construction of one single-family home of average size creates the equivalent of nearly 3 new jobs for a year (Greiner, 2015). The economic impact is significant, with residential construction and related activities contributing approximately 5 percent to overall gross domestic product. With these data points in mind, the ability to accurately predict housing trends has become an increasingly important function for organizations engaged in the real estate market. The government bailouts of Freddie Mac and Fannie Mae in July 2008, following the severe housing market collapse which began earlier that year, serve as an example of the risks associated with the housing market. The housing market collapse had left the two firms, which at the time owned or guaranteed about $5 trillion of home loans, in a dangerous and uncertain financial state (Olick, 2018). Countrywide Home Loans, Indy Mac, and Washington Mutual Bank are a few examples of mortgage banks that did not survive the housing market collapse and subsequent recession. In the wake of the financial crisis, businesses within the real estate market have recognized that predicting the direction of real estate is an essential business requirement. A business acquisition by Radian Group, the Philadelphia-based mortgage insurance company, illustrates the importance of predictive modeling for the mortgage industry. In January 2019, Radian Group acquired Five Bridges Advisors, a Maryland-based firm which develops data analytics and econometric predictive models leveraging artificial intelligence and machine learning techniques (Blumenthal, 2019)

REAL ESTATE PRICE PREDICTION USING ARTIFICIAL NEURAL NETWORK WITH L1 REGULARIZER AND WITHOUT REGULARIZER

In the real estate industry many real estate agencies and independent brokers facing numerous factors that can impact their business process and outcomes, resulting many agencies and independent brokers began to estimate the value of property / real estate with the help of machine learning to determine which real estate listing should be prioritized in order to be sold. In recent studies two different models of machine learning using Regression Models and Neural Networks performing price prediction on some real estate data and resulting regression models were overestimated and neural networks less. Also that the neural network insufficient on validation sample quality may cause overestimation of house prices to market prices. In this paper, provided sequence real estate data with a total 81 columns, the researcher conducts different Artificial Neural Network (ANN) where the first ANN using L1 regularization while the other ANN without L1 regularization. Before conducting different model on 2 machine learning models, the researcher did some preprocessing data using Z-score normalization and Min-max scaling also that before scaling and standarized the data, the author also done feature selection using Pearson Correlation Coefficient analysis to the 81 features so that when research only use important features. After selected the features into different multiple ranges, then the selected features of train and validation trained into 2 different Artificial Neural Network models and which we splitted the train, test, and validation data with sizes 70% - 15% - 15% respectively. The researcher expect the best model performance evaluated using RMSE, MAE, MAPE, and R-Squared was the Artificial Neural Network (ANN) with L1 regularizer that resulting evaluation values 114430, 86332, 0.4871, -2.1965 respectively. The optimum hyperparameter were 100 epochs / learning intervals, Adam as the learning algorithm, 600 batch size in each epochs, use hyperbolic tanh (Tanh) as the input activaton, logistic (Sigmoid) as the hidden activation and exponential linear unit (ELU), 1e-12 as the alpha / learning rate per epochs, data features and target scaled and notinverse scaled using MinMaxScaler, also we also done Pearson Correlation Coefficient to the each features with values range 0.1 to 1. The logistic (Sigmoid) activation function are causing the RMSE and MAE value bigger then the standard deviation of the target variable : 79442 because it was hard when vanishing the gradient problem

Office Rent Prediction based on the Influenced Features

This study applies a new approach in identifying the best Machine Learning model to predict office rent and determining the most significant factors influencing rental values. The Auto Model uses three (3) distinct types of Machine Learning algorithms, namely the Decision Tree, Random Forest, and Support Vector Machine. The Auto Model highlights that the Decision Tree outperformed Random Forest and Support Vector Machine for better prediction. The results of statistical analysis using Auto Model suggest that among the factors that influence office building rental, amenities, and in-house services show significant roles in the model. Keywords: Office Rent, Machine Learning, Prediction eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI

Predicting The Resale Asking Price Of Wind Turbines Using A ML Model Trained On Data From An Online Resale Platform

Around half of the currently 30,000 active wind turbines in Germany will reach the end of their service life by 2030, which is generally defined by the manufacturer as 20 years of operation. The most common strategy for the subsequent use of a wind farm is repowering, provided this is (legally) possible at the respective location. One option for dealing with old turbines is to resell them. At the time of repowering, in Germany after an average of around 17 years, the wind turbines usually still have a remaining operating time of several years before critical parts such as generators fail. This article presents a machine learning model for predicting the resale value of used wind turbines. This model can be used to approximately predict the resale value of comparable wind turbines based on certain input parameters such as the power output or the age of the wind turbines. The model was trained using an adjusted data set from an online trading platform for wind turbines. The necessary pre-processing steps such as the removal of extreme outlier values and the addition or replacement of missing or incorrect wind turbinespecific data from a second data source using a self-developed matching algorithm are presented. Finally, the prediction accuracy of different ML algorithms is tested using test data to find the best method for predicting the resale value of wind turbines

Who performs better? AVMs vs hedonic models

Purpose: In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis. Design/methodology/approach: All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other. Findings: Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities. Practical implications: AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical. Originality/value: According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained

The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach

This paper follows the recent literature on real estate price prediction and proposes to take advantage of machine learning techniques to better explain which variables are more important in describing the real estate market evolution. We apply the random forest algorithm on London real estate data and analyze the local variables that influence the interaction between housing demand, supply and price. The variables choice is based on an urban point of view, where the main force driving the market is the interaction between local factors like population growth, net migration, new buildings and net supply

Developing Real Estate Automated Valuation Models by Learning from Heterogeneous Data Sources

In this paper we propose a data acquisition methodology, and a Machine Learning solution for the partially automated evaluation of real estate properties. The novelty and importance of the approach lies in two aspects: (1) when compared to Automated Valuation Models (AVMs) as available to real estate operators, it is highly adaptive and non-parametric, and integrates diverse data sources; (2) when compared to Machine Learning literature that has addressed real estate applications, it is more directly linked to the actual business processes of appraisal companies: in this context prices that are advertised online are normally not the most relevant source of information, while an appraisal document must be proposed by an expert and approved by a validator, possibly with the help of technological tools. We describe a case study using a set of 7988 appraisal documents for residential properties in Turin, Italy. Open data were also used, including location, nearby points of interest, comparable property prices, and the Italian revenue service area code. The observed mean error as measured on an independent test set was around 21 K€, for an average property value of about 190 K€. The AVM described here can help the stakeholders in this process (experts, appraisal company) to provide a reference price to be used by the expert, to allow the appraisal company to validate their evaluations in a faster and cheaper way, to help the expert in listing a set of comparable properties, that need to be included in the appraisal document