An Exhaustive Analysis of Lazy vs. Eager Learning Methods for Real-Estate Property Investment

1 INTRODUCTION . Real estate rent prediction has a key role in calculating the Rate of Return- a measure for evaluating the performance of an investment in the housing market . Rate-of-Return can measure the quality of a real estate investment over a time-period and Net Present Value ( NPV ) is one of its important factors ( Investopedia ) 1 . In real-estate NPV defines the profitability of the investment based on rate of return . The NPV yields an accurate insight to real estate investors on whether they achieve a satisfactory rate of return within a certain period of time . Equation 1 shows how to calculate NPV2 : NPV = N∑ n=0 CFn ( 1 + r ) n ( 1 ) In equation 1 , CF is the cash flow generated from a rental property for each period n in the holding period N ( i.e. , the time period of an investment ) , and r is the desired investor rate of return . Based on equation 1 , one of the important factors in evaluating the NPV of a real estate property investment is cash flow ( CF ) that is calculated based on equation 2 for a 12-month period : 1https : //www.investopedia.com/terms/f/futurevalue.asp 2 https : //www.propertymetrics.com/blog/2015/06/11-/what-is-npv/ CF = 11ρ− ( 12µ+ τ + + ι ) ( 2 ) where ρ is the rent income ( for simplicity , the Vacancy rate analysis is not discussed here , and the assumption is based on 11-months rent income per year ) , µ is the house mortgage , τ is the annual house tax , is the annual house expenses , and ι is the mortgage house insurance . In equation 2 , ρ is only factor that has a positive effect on cash flow . Therefore , it is very essential to provide an accurate rent prediction method . For many people , house is an invaluable asset . Therefore , having a safe investment is a significant task . Proper rental property investments can lead to a successful and profitable Rate of Return over time . However , such ventures can be very risky due to miscalculation or inaccuracy of algorithms used in rent prediction . Applying machine learning algorithms to perform house rent prediction is not a novel trend . Lambert & Greenland ( 2015 ) investigates eager learning methods like MLP , and bagging REP trees to estimate the rental rate for both the land-owners and students interested in renting a place close to a university campus . The coverage area of the training set is limited to three distant zip codes surrounding a university campus . The input features entertained in this work include proximity to university campus , apartment appliances and dimensions , the length of the apartment contract , and the date of the residence ’ s constructions . The study reports bagging REP trees as the best rent prediction algorithm . However , the proposed global learningbased solution can generate a biased model due to the skewed data set , all located surrounding a university campus . In the previous studies , the prediction models for real estate rent/price prediction are very generic and they don ’ t differentiate according to the house type and/or zip code ( Limsombunchai , 2004 ; Yu & Wu , 2016 ) . Figure 1 shows zip code-wise variation of the rent behavior , even for the real estate properties which are in the same state/city or a close geo-spatial proximity from each other . For instance , the zip codes 22066 and 20190 are neighbors but they show a very different behavior in terms of the average rent price . In addition , statistical data shows that house type parameter affects the rent price due to internal factors like area space , number of bed/bathrooms , HOA fee , community factors , so forth ( see Figure 3 ) . The average rent price for a zip code depends on internal factors ( like house type , number of bed/bath , price , area space , other ) and external factors . In fact , external factors like walk/transit score , crime rate , and school ratings corresponding to a zip code impact the price of rent and are deal-breakers for many real estate investors ( US News ) 3 . In this paper , internal and external house properties are entertained . Walk score indicates the errands that can be accomplished on foot or those that require a car to nearby amenities . Transit score indicates the connectivity ( i.e. , proximity 3https : //realestate.usnews.com/real-estate/articles/ how-homicide-affects-home-values to metro ) , access to jobs , and frequency of service . Crime score indicates the rate of violent and non-violent incidents related to a zip code ( FBI ) 4 . In the past studies , the impact of eager learning methods for real estate rent/price prediction has been investigated ( Lambert & Greenland , 2015 ) . Eager methods require less space in comparison with lazy algorithms . However , in the real estate rent prediction domain , we are not dealing with streaming data , and so data volume is not a critical issue . In general , unlike eager learning methods , lazy learning ( or instance learning ) techniques aim at finding the local optimal solutions for each test instance . Kohavi et al . ( 1996 ) and Homayouni et al . ( 2010 ) store the training instances and delay the generalization until a new instance arrives . Another work carried out by Galván et al . ( 2011 ) , compares a limited number of memory-based learning vs neural network methods , and report the superiority of memory-based method over Neural Network approach using multiple data sets of UCI machine learning data set repository , including Iris , Diabetes , Sonar , Vehicle , car , and balance . In this study , the two lazy learning algorithms , namely Integer Part and Atomic Radios outperformed eager learning algorithms , namely SMO and Naive Bayes algorithm . Our work is inspired by these research explorations . We collected a Zillow data set of 4K real estate properties in Virginia State . In addition , transit/walk score , and crime rate are collected from information sources like alltransit.cnt.org , walkscore.com , and crimereports.com respectively . The transit parameters entertained in the data collected from AllTransit data source clearly indicates the proximity to metro as a significant parameter in determining the transit score of a location . The data set consists of three house types : town-house , single family , and condo . This study is motivated by the need to build models with respect to house type and zip code . To deal with the sparse ( i.e. , thin ) data in every zip code , we divided the data set according to house type , and then applied K-means clustering to generate subsets of instances within zip codes with similar average rent prices as illustrated in Figure 2 . The clustering method uses the similarity measure of average-rent to compute the distance between the data points . The data samples in each cluster is later used to train a rent prediction model . In this work , we study the impact of several machine learning methods on this data set by performing a comparative analysis of lazy vs. eager learning methods . We report the empirical results to compare the performance of lazy vs. eager learning algorithms in real-estate rent prediction for each house type and a subset of zip codes with similar average rent prices . We examine the performance of Linear Regression ( LR ) , Sequential Minimal Optimization ( SMO ) , Multilayer Perceptron ( MLP ) , J48 , SVM , and Random Forest ( FR ) algorithms ( eager/globally-based learning ) against K-Nearest Neighbors ( KNN ) , Multi-Label KNN ( ML-KNN ) , lazy Decision Tree ( Lazy-DT ) , locally weighted learning ( LWL ) and KStar ( K * ) algorithms ( lazy/memory-based learning/instancebased ) , using two performance evaluation metrics : i ) R-squared and ii ) Mean Absolute Error ( MAE ) . The target variable is the rent price and the evaluation metrics show the variance between the predicted target variable and the actual rent price . Our rent prediction algorithm uses a salient subset of data set attributes , which is determined during feature selection using Principal Component Analysis technique ( PCA ) . PCA technique filters out unwanted features based on each house type ( i.e. , single family , town house , and condo ) , and independent of the learning algorithm applied on the data . For 4https : //ucr.fbi.gov/crime-in-the-u.s/2011/crime-in-the-u.s.-2011/ violent-crime/violent-crime imputation , we removed the observations with many missing attributes as the proportion of these instances to the entire data set was less than 3 % . The remainder of this paper is structured as follows : In section 2 related work is discussed . We describe the data set used in this paper for analysis in section 3 . Section 4 describes our methodological framework including data preprocessing , data exploration and feature selection , building prediction models , and model evaluation . In section 5 , experiments and results are discussed . Finally , section 6 gives the conclusion . 2 RELATED WORK . Real estate rent/price prediction using machine learning techniques has already been studied in several works ( Galván et al. , 2011 ; Limsombunchai , 2004 ; Khamis & Kamarudin , 2014 ; Basu & Thibodeau , 1998 ; Webb , 2011 ; Kuntz & Helbich , 2014 ) . In Wang et al . ( 2014 ) , PSO-SVM algorithm is used for real-estate price prediction . Liu ( 2013 ) uses spatiotemporal dependencies between housing transactions to predict future house prices . This approach is limited by spatial autocorrelation , since the degree of similarity between observations is not solely based on the distance separating them . Some of the previous work focus on hedonic price models as a method of estimating the demand and value in the housing market and determination of house prices ( Önder et al. , 2004 ; Ozus et al. , 2007 ) . In these studies , rather than internal and external house features , economic submarkets are used in the prediction model which are defined in terms of the characteristics of neighborhoods or census units . Limsombunchai ( 2004 ) uses a sample size of 200 houses of all house types in New Zealandto train a hedonic price and an artificial neural network ( ANN ) model , and shows that the eager method ANN outperforms the hedonic model . The problem with the hedonic approach is disregarding the differences between the properties in the same geographical area . Park & Bae ( 2015 ) determine the house sales trends of the US housing market subject to the Standard & Poor ’ s CaseShiller home price indices and OFHEO which is the housing price index of the Office of Federal Housing Enterprise Oversight . The authors used and compared the classification accuracy of four methods C4.5 , RIPPER , Naı̈ve Bayesian , and AdaBoost , where RIPPER algorithm outperformed others . We are now at the age where people in different fields are hacking their way into machine learning . Machine learning techniques have become available as commodities which can be used to perform prediction and classification tasks in various domains like real-estate rent/price prediction . Khamis & Kamarudin ( 2014 ) compared the efficacy of the eager learning method Neural Network ( NN ) against the hedonic model Multiple-Linear Regression ( MLR ) , and showed that NN outperforms MLR . However , Galván et al . ( 2011 ) reports the superiority of lazy learning methods over NN . According to Webb ( 2011 ) , eager learning methods can sometimes lead to suboptimal predictions because of deriving a single model that seeks to minimize the average error over the entire data set , whereas lazy learning can help improve prediction accuracy . While our study is inspired by Galván et al . ( 2011 ) and Webb ( 2011 ) , we take our analysis to the next level , by comparing the impact of eager and lazy learning algorithms in the prediction accuracy of the generated models with respect to each house type and a subset of zip codes with similar average rent prices . We use a two-layer clustering technique , and a subset of internal and external real-estate property factors .

Summary: The authors compare a collection of machine learning models to predict the expected rental income from an investment property. The dataset they use to train their model is fairly small (around 4K transactions). In addition to using house specific features the authors use other macro features, such as, walk score etc. Using this the authors compare a set of machine learning models and report their findings.