Title: An Exhaustive Analysis of Lazy vs. Eager Learning Methods for Real-Estate Property Investment. Abstract: Accurate rent prediction in real estate investment can help in generating capital gains and guaranty a financial success. In this paper, we carry out a comprehensive analysis and study of eleven machine learning algorithms for rent prediction, including Linear Regression, Multilayer Perceptron, Random Forest, KNN, ML-KNN, Locally Weighted Learning, SMO, SVM, J48, lazy Decision Tree (i.e., lazy DT), and KStar algorithms. 
Our contribution in this paper is twofold: (1) We present a comprehensive analysis of internal and external attributes of a real-estate housing dataset and their correlation with rental prices. (2) We use rental prediction as a platform to study and compare the performance of eager vs. lazy machine learning methods using myriad of ML algorithms.  
We train our rent prediction models using a Zillow data set of 4K real estate properties in Virginia State of the US, including three house types of single-family, townhouse, and condo. Each data instance in the dataset has 21 internal attributes (e.g., area space, price, number of bed/bath, rent, school rating, so forth). In addition to Zillow data, external attributes like walk/transit score, and crime rate are collected from online data sources. A subset of the collected features - determined by the PCA technique-  are selected to tune the parameters of the prediction models. We employ a hierarchical clustering approach to cluster the data based on two factors of house type, and average rent estimate of zip codes. We evaluate and compare the efficacy of the tuned prediction models based on two metrics of R-squared and Mean Absolute Error, applied on unseen data. Based on our study, lazy models like KStar lead to higher accuracy and lower prediction error compared to eager methods like J48 and LR. However, it is not necessarily found to be an overarching conclusion drawn from the comparison between all the lazy and eager methods in this work. 