Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).
This research applies neural network (NN) technology to real estate appraisal and compares the performance of two NN models in estimating the sales price of residential properties with a traditional multiple regression model. The study is based on 288 sales of homes in Fort Collins, Colorado. Results do not support previous findings that NNs are a superior tool for appraisal analysis. Furthermore, significant problems were encountered with the NN models: inconsistent results between packages, inconsistent results between runs of the same package, and long run times. Any appraiser who plans on using this new technology would do so with caution. This paper compares the predictive performance of artificial neural networks (ANN) and multiple regression analysis (MRA) for single family housing sales. Multiple comparisons are made between the two data models in which the data sample size is varied, the funcional specifications is varied, and the temporal prediction is varied. We conclude that ANN performs better than MRA when a moderate to large data sample size is used. For our application, this "moderate to large data sample size" varied from 13 to 39 of the total data sample (506 to 1506 observations out of 3906 total observations). Our results give a plausible explanation why previous papers have obtained varied results when comparing MRA and ANN predictive performance for housing values. We use hedonic analysis of home transaction data from the Minneapolis–St. Paul metropolitan area to estimate the effects of proximity to open space on sales price. We allow the effects of proximity to vary with demographic and location-specific characteristics and include fixed effects to control for observed and unobserved neighborhood characteristics. We find that the value of proximity to open space is higher in neighborhoods that are dense, near the central business district, high-income, high-crime, or home to many children. Using the metropolitan area's average value may substantially overestimate or underestimate the value of open space in particular neighborhoods. Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches - support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center. 