# RECORD ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f # TITLE: An ANN Model for the Influence of Siding Materials on Single-Family Home Values # YEAR: 2003.0 VENUE: nan DOI: 10.18260/1-2--12549 # AUTHORS: Mohammed E. Haque; A. Taibah # PAGES: 9 CANDIDATE TABLE PAGES (1-based): [5] # ABSTRACT: The real estate valuation is a complex process considering the range of variables that are known to play a role in determining such a value. The esthetics and choice of building’s facade materials can greatly influence a homebuyer’s decision. This paper evaluated such buyer’s preferences of various siding materials by estimating the market-clearing prices determined by the buyers of single-family homes (SFHs). The study employs a comparative hedonic estimation and artificial neural network (ANN) model. The study relied on a large sample of SFHs that were sold during the period from 1997 to 2000 in College Station, Texas. The analysis was restricted to homes that are highly homogeneous in their structural attributes in order to eliminate the impact of such attributes on the home values. The main aim of this study was to develop an ANN model to determine the influence of four different siding materials on home values. Several different ANN models with different layers/slabs connections, weights, and activation functions were trained. The presented ANN Jordan-Elman Nets with logistic activation function was the best one among all other trials. It converged very rapidly to produce a very high predictive efficacy. The trained model was evaluated using the data that was not used during the training, which also indicated very good agreement between the actual and ANN predicted price. Additionally the study was aimed to compare the hedonic value estimation and ANN value prediction methods. The results indicate that stucco siding had the most significant impact on the property value. The estimated implicit values of different siding materials were a measure of the importance of such materials to the homebuyer and was resembled in the form of a paid premium. Moreover, in comparing the hedonic results and ANN results, this study found that both analytical methods support one another and have assigned similar weights to the various construction types that have been studied. In addition ANN showed to have a higher predictive accuracy level than did the hedonic estimation. The findings extend the body of literature concerned with real estate value analysis and have significant implications in the realm of fund allocation decision making for a real estate developer. ===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) ===== Session 1106 Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright  2003, American Society for Engineering Education An ANN Model for the Influence of Siding Materials on Single-Family Home Values Mohammed E. Haque, Ph.D., P.E., Amro Taibah, Ph.D. Texas A&M University, Texas, USA/ King Abdulaziz University, Saudi Arabia Abstract The real estate valuation is a complex process considering the range of variables that are known to play a role in determining such a value. The esthetics and choice of building’s façade materials can greatly influence a homebuyer’s decision. This paper evaluated such buyer’s preferences of various siding materials by estimating the market-clearing prices determined by the buyers of single-family homes (SFHs). The study employs a comparative hedonic estimation and artificial neural network (ANN) model. The study relied on a large sample of SFHs that were sold during the period from 1997 to 2000 in College Station, Texas. The analysis was restricted to homes that are highly homogeneous in their structural attributes in order to eliminate the impact of such attributes on the home values. The main aim of this study was to develop an ANN model to determine the influence of four different siding materials on home values. Several different ANN models with different layers/slabs connections, weights, and activation functions were trained. The presented ANN Jordan-Elman Nets with logistic activation function was the best one among all other trials. It converged very rapidly to produce a very high predictive efficacy. The trained model was evaluated using the data that was not used during the training, which also indicated very good agreement between the actual and ANN predicted price. Additionally the study was aimed to compare the hedonic value estimation and ANN value prediction methods. The results indicate that stucco siding had the most significant impact on the property value. The estimated implicit values of different siding materials were a measure of the importance of such materials to the homebuyer and was resembled in the form of a paid premium. Moreover, in comparing the hedonic results and ANN results, this study found that both analytical methods support one another and have assigned similar weights to the various construction types that have been studied. In addition ANN showed to have a higher predictive accuracy level than did the hedonic estimation. The findings extend the body of literature concerned with real estate value analysis and have significant implications in the realm of fund allocation decision making for a real estate developer. Introduction Artificial intelligence (AI) applications have gained a broad interest in civil/construction/ architectural engineering problems. Its applications are very extensive and interdisciplinary. The graduate students civil/construction/architectural students should especially be encouraged to learn various applications of computing techniques including artificial neural network (ANN), genetic algorithm (GA), etc. This paper highlights various applications of AI. As an example of Page 8.181.1 ===== PAGE 5 (score 30, numeric-paren 5, table-words 8, var-words 9) ===== Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright  2003, American Society for Engineering Education Results of Hedonic Procedure An analysis of the data was performed using the hedonic estimation procedure in order to ascertain the relationship between property market value and the type of siding material used. The hedonic price function describes the relationship between the observed prices of commodities and the characteristics associated with the commodities, based on the hypothesis that “goods are valued for their utility-bearing attributes or characteristics,” 12. Hence the coefficient estimates represent the premium paid by the homeowner for the aesthetic utility that any siding material might bare. Based on the results of the analysis, the hedonic equation can be written as follows: PPSQFT00 = 52.419 - 0.515*AGE_YR + 16.595*CONST_VENEER + 15.734* CONST_FRAME + 18.323*CONST_MIX +37.642*CONST_STUCO + e Where e = error term. The F-value of the model was found to be statistically significant at the 0.0001 levels. The F statistic basically tests how well the model, as a whole, accounts for the dependent variable's behavior. The estimation efficacy of the model was found to be moderately high with a coefficient of determination (R2) of 0.622. This is an indication that the model is able to explain approximately 62% of the total variation in the property’s market value. The results of this study indicate that “construction type” is an important attribute that has a significant effect on SFH market value. Moreover the findings indicate that certain types of construction have a more considerable impact than others. For example, based on the study sample, it was found that using various types of stucco siding in exterior finishing could have a noticeably higher impact on future market price than other sidings. Compared to brick veneer, which is the most common exterior finishing material in the College Station market, Stucco finishing was found to reflect an increase difference of approximately $21 per square foot in market price. In addition it was found that plywood sidings had the least premium estimated at approximately $15 per square foot. Homes with mixed siding materials in the sample were mainly brick veneer mixed with either wood or stucco sidings. This category also showed an estimated hedonic price of approximately $18 per square foot. ANN Model and Results The parameter inputs considered in the proposed ANN model were the ones that were found to be statistically significant using the hedonic procedure (AGE_YR, CONST_VENEER, CONST_FRAME, CONST_MIX, CONST_STUCO). The output parameter was price per square foot adjusted to 2000 value (PPSQFT00). During training the model, 130 observations were used out of a total of 145 observations. The remaining 15 observations, separated arbitrarily from the total number of observations, were used during testing the trained model. Several different ANN models with different layers/slabs connections, weights, and activation functions (including logistic, linear, Symmetric Logistic, and Gaussian) were trained. In addition, pattern selections including "Rotation" and "Random" were applied with weight updates using Vanilla, Momentum and TurboProp. The presented ANN- Jordan-Elman recurrent network with linear and logistic mixed activation function ("Rotation" for pattern selection, and "Momentum" for weight updates) was the best one among all other trials. In this research, Jordan-Elman recurrent network converged very rapidly to produce a very high predictive efficacy. Fig. 3 shows the Page 8.181.5 ===== PAGE 6 (score 11, numeric-paren 0, table-words 3, var-words 5) ===== Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright  2003, American Society for Engineering Education -40 -20 0 20 40 60 80 100 120 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 Pattern Processed Price $ per Sq. Ft. Actual Price ANN Predicted Price Errors Fig. 3 Actual and ANN Predicted Price during the Network Training Phase 0 20 40 60 80 100 120 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Pattern Processed Price $ per Sq. Ft. Actual Price ANN Predicted Price Fig. 4 Actual and ANN Predicted Price during the Network Evaluation actual and ANN predicted price ($ per Sq. Ft.) during the Network Training Phase, which indicates very low errors between the actual price and the ANN predicted price. The trained model was evaluated using the data that was not used during the training. Fig. 4 depicts the actual and ANN Predicted Price during the network evaluation phase, which indicates very small difference between the actual and ANN predicted price. During network training, R2 was obtained as 0.7788, and 0.8627 during network evaluation, which were very close to 1.0, indicating a very good fit between the actual and the network prediction. R2 is a statistical indicator usually applied to multiple regression analysis, and was calculated using the following formulae13: R2 = 1 - (SSE/SSyy) Where SSE = Σ (y - y)2, SSyy = Σ (y - y)2, y is the actual value, y is the predicted value of y, and y is the mean of the y values. ANN Price Prediction Hypothetical data was developed in order to compare the performance of the hedonic estimation model and the ANN model. The purpose is to further the validation and evaluate the performance similarities or differences between the two methods. Ten imaginary data points were used with building ages of 5 and 10 years old and siding material characteristics that were chosen arbitrarily. The two Page 8.181.6