Paper:
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 techniqueare 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.

Review:
The paper investigates different machine learning approaches to model and
predict the return on property investments, in particular with respect to eager
and lazy learning techniques. The authors evaluate those different techniques on
a dataset of properties in Virginia. They conclude that lazy techniques provide
better performance than eager ones.
The paper is a purely empirical study that does not introduce any novel
machine learning or evaluation techniques. The authors use the off-the-shelf
WEKA toolbox. The results are not clear, given that only a single data set was
used to evaluate the different approaches, and general recommendations cannot be
made.
The paper is not well written and the descriptions do not convey what the
authors have done very well. An example of this is Figure 3, which purports to
show the average rent (or rent distribution?) for different housing types. There
are multiple categories in there that are not valid housing types ("Make Me
Move") and the rents shown are incorrect (e.g. more than a million for a town
house). It is also unclear why the average rent for a single family house is
approximately 4 times as much as for a town house.
The problems with Figure 3 are exemplary of the paper; the other issues are too
numerous to list.
In summary, this paper should be rejected.