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It is challenging to make precise assessments of real estate prices due to its elevated individual prices, complicated influencing factors, and ambiguous attribute selection. As a result of the high demand for owner-occupied and investment properties, real estate is also a substantial concern for society. A hot topic for research by major institutions has been how to accurately estimate its price. Real world applications of real estate valuation impose stringent requirements on the acquisition of datasets and the generalizability of models. On the basis of SRGCNN, a spatial regression model with excellent generalizability, this paper introduces an external attention mechanism to construct the A-SRGCNN model and compares it to the benchmark model utilizing data from Shanghai, Melbourne, and San Diego. For spatial regression, A-SRGCNN employs graph convolutional neural networks, and the external attention mechanism implicitly considers the relationship between property data. Experiments indicate that the A-SRGCNN model outperforms the benchmark model and has improved real estate price estimation accuracy. In the meantime, this paper employs the A-SRGCNN model to conduct zonal experiments and time-division experiments on the secondary real estate market in Shanghai to analyze the real estate price linkages between different zones and the real estate price linkages at different times. It is revealed that Shanghai real estate prices exhibit spatial aggregation and price aggregation, with comparable prices within the same zones, and that the A-SRGCNN model is effective at predicting house prices.
The hedonic framework was originally employed by S. Rosen to examine the relationship between real estate prices and the living environment [1]. R. Meese et al. utilized hedonic regression models to valuate the dynamic impact of market fundamentals on real estate prices [2]. After many years of development, the hedonic model has become an established method of real estate price valuation, utilized in a large number of appraisal models and serving as a crucial foundation for bank loan approvals and government monetary policies. However, the hedonic method also has some drawbacks, such as the fact that the results of the hedonic model can vary depending on the estimation formula or process selected, which enhances the subjectivity of the appraisal, and necessitates a high demand for analysts with specialized knowledge, and necessitates a large quantity of property price data. The field of machine learning is another area of research. Earlier stage machine learning models for estimating house values were relatively homogenous and relied on straightforward statistical and mathematical techniques like regression analysis.In multiple regression analysis, R. Dubin et al. used spatial regression techniques to estimate home prices [3]. However, this method ignores the impact of time variation. Real estate price changes can be thought of as a time series because real estate prices are affected by time characteristics as well. To forecast the growth of home prices in four US regions, R. Gupta et al. used a time series model with dynamic factor analysis and Bayesian shrinkage estimation [4]. Time series can also be incorporated into spatial regression models. In order to account for spatial and temporal heterogeneity, B. Huang et al. incorporate time effects into GWR models to assess house prices [5].Although these methods' performance in making forecasts is acceptable, their use in determining actual house prices is very limited. Although these methods' valuation performance is acceptable, there is very little use for them to determine real estate prices. The variables influencing real estate prices are intricate, making it challenging to monitor price changes. It is incredibly difficult for standard mathematical models to accurately model estate prices. Hedonic models have gained in popularity over the past few decades due to their affordability, accuracy, and complexity. Due to deep learning's strong computational capabilities and its many benefits in interdisciplinary fields, complex fitting is now possible. Real estate price valuation is beginning to use deep learning. A lot of people use artificial neural networks (ANN). H. Selim used an artificial neural network model to predict Turkey's real estate prices and noticed that it performed much better than the characteristic price model [6]. When compared to the hedonic model [7], S. Peterson et al. use of ANN on a sizable sample of 46,467 residential data revealed that it performs better when there are a lot of dummy variables because parameter estimation for ANN does not depend on the rank of the regression matrix. Because semi-supervised learning better takes advantage of the nonlinear relationships between the factors involved, Y. Guo et al. discovered that applying a semi-supervised learning strategy to ANN estate prediction can achieve similar or even superior performance compared to a fully supervised ANN method [8]. To estimate house prices in London, UK, S. Law et al. combined housing character traits with a deep neural network model [9]. Disadvantages ? An existing methodology doesn’t implement SPATIAL REGRESSION GRAPH CONVOLUTION NEURAL NETWORK REAL ESTATE PRICE VALUATION MODEL BASED ON EXTERNAL ATTENTION MECHANISM (A-SRGCNN) method. ? The system implemented RNN and CNN methods for the datasets which are less effective.
The proposed system presumes that the A-SRGCNN model is appropriate for real estate valuation since real estate price appraisal is indeed a very typical spatial regression scenario, and the SRGCNN appraisal model performs well in this scenario in comparison to older models [14]. What's more, the external attention mechanism can delve deeper into the linkage between sample data, which corresponds to the close connection among real estate data, so the model would further work on improving the valuation consistent manner on the SRGCNN valuation model's impressive performance. Real estate appraisal models have high requirements in terms of their ability to generalize over validation sets and different data sets. The acquisition of certain attributes for real estate data samples regularly presents some challenges. Consequently, the A-SRGCNN real estate appraisal model is constructed in this paper. The model is based on the spatial regression model SRGCNN, and the spatial regression algorithm shows good generalization ability and stable performance when it comes to different datasets. The most important parameter of the SRGCNN model is the spatial location of real estate, and the spatial information of real estate is often easier to obtain in reality. The A-SRGCNN approach incorporates an attention mechanism by adding an external attention layer before the final output, which is based on the use of the SRGCNN model. There are tight connections between real estate samples, and the external attention layer enhances the algorithm's truthfulness by capturing the global connections between property samples via shared memory units. Accordingly, compared with popular regression valuation models, the A-SRGCNN proposed in this paper is more generalizable, performs stably on different samples, and the attributes of the required samples are easily available, which is more in line with the realistic needs of real estate valuation, while taking into account the accuracy of the valuation. Advantages ? The System proposes Qualitative analysis and quantitative analysis are the two most common categories used to objectively assess real estate prices. ? In this work, the proposed system implemented SPATIAL REGRESSION GRAPH CONVOLUTION NEURAL NETWORK REAL ESTATE PRICE VALUATION MODEL BASED ON EXTERNAL ATTENTION MECHANISM (A-SRGCNN) which is more effective and Accurate.