A Technical Report on Real-Estate Rent Prediction




Rafatirad, Setareh

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Real-estate rent prediction is sensitive to several independent parameters and has allured a lot of researchers in the past few years to constructing automated tools using (ML) commodities. However, most of the proposed solutions are limited in scope, and are only investigated on a particular locality, house type, or based on one type of machine learning algorithm. Furthermore, the past work often used synthetic data which can compromise the accuracy of the output, as it is not closely identical to real-world datasets. To address these challenges, we study a wide range of Machine Learning techniques applied to three real-estate housing types, using real-world data. Unlike prior work which attempt to develop a one-size-fits-all model with fixed set of features, our study shows that the important parameters for rent prediction depends highly on the type and locality. Further, for each property type, there is a different winning algorithm to perform rent prediction. Accordingly, we construct multiple rent prediction models using a large Zillow dataset of 50K real estate properties in the state of Virginia and Maryland. In addition to Zillow, external attributes such as walk/transit score, and crime rate are collected from online sources. Our comprehensive case study indicates that real-estate rent behavior strongly depends on the type of house and locality. As such, we deploy a two-layer clustering approach to partition data into multiple training sets based on house-type and similar zip codes. We evaluate and report the performance of the prediction models studied in this work based on two metrics of R-squared and Mean Absolute Error, applied on unseen data.



Real estate, Machine learning