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The illustration of the spatial distribution of houses and latent geographical presentation in the city of Los Angeles on street map
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The geographical presentation of a house, which refers to the sightseeing and topography near the house, is a critical factor to a house buyer. The street map is a type of common data in our daily life, which contains natural geographical presentation. This paper sources real estate data and corresponding street maps of houses in the city of Los An...
Citations
... Previous works defined real estate appraisal as a supervised regression problem, and addressed it with machine learning techniques [11,15,27]. To incorporate multimodal data sources for improving the performance, Zhao et al. [29] took the visual content of rooms into account using a deep learning framework with XGBoost [5], while Bin et al. [4] utilized street map images with attention-based neural networks. On the other hand, LUCE was proposed to tackle spatial and temporal sparsity with the lifelong learning heterogeneous information network consisting of graph convolutional networks and long short-term memory networks [21]. ...
The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.
... They showed the model is superior to the spatial autoregressive (SAR) model, and using a wider surrounding area of the target house improved model performance. Similarly, Bin et al. (2019) included street maps in their CNN-based valuation model to consider latent geographical features from the open street map and a birds-eye view image of the neighbourhood. ...
... other countries or cities). Also, most of related studies randomly split training and validation datasets (Bency et al. 2017, Bin et al. 2019, Kang et al. 2020, Kostic and Jevremovic 2020, Gao et al. 2022. In this context, we randomly split train and validation data instead of using spatially stratified split. ...
... Also, recent studies related to this study show a similar level of improvement in their results. Bin et al. (2019) suggested an improvement of 2.6% in MAPE between the baseline model, a boosting regression model (20.1% in MAPE), and Attention-based Multi-Modal Fusion (AMMF) that a proposed method in this study. Poursaeed et al. (2018) trained CNN to measure the luxury level of the area at the house such as the bedroom, bathroom, living room, and kitchen using pictures of the house. ...
Geographic location and neighbourhood attributes are major contributors to residential property values. Automated valuation models (AVM) often use hedonic pricing with location and neighbourhood attributes in the form of numeric and categorical variables. This paper proposed a novel approach to automated property valuation using a machine learning model with a convolutional neural network (CNN), fully connected neural network layers with numeric and categorical variables. In this study we compare the results of a fused model, which treat geographical data as an input with the performance of the baseline neural network model with only numerically or categorically represented data. Furthermore, the residential valuation by the proposed fused model was tested with actual sold price data in Greater Sydney, Australia. The study found that the fused model produced valuations with a significantly lower mean absolute percentage error (MAPE) (8.71%) than the MAPE of the baseline model (11.59%). The results show that the fused model with CNN significantly improves the accuracy for residential valuation, reducing spatial information loss by data manipulation and distance calibration.
... The complexities could be defined as complex interconnections or nonlinear relationships among the variables that must be accounted for in the valuation process [21]. The valuation process often involves a large amount of data, which limits the accuracy of hedonic models but now, such big data can be handled by data mining models [22]. Traditional valuation models are not fully flexible in reducing the effects of complexities and uncertainties inherent in the valuation process [23]. ...
Professional service firms employ strategic management (SM) in achieving their long term goals. Estate valuation and surveying firms, a subset of professional firms, are not an outlier in SM practices and strategic management tools and techniques (SMTT) usage. The aim of this paper is to predict the highest professional qualification of Estate Surveyors and Valuers (ESVs) in Southwest Nigeria, applying the degree of usage of strategic management tools and techniques (SMTTs) and years of professional experience. In this context, the highest professional qualification is being a Fellow of the Nigerian Institution of Estate Surveyors and Valuers (FNIESV), a step above being an Associate (ANIESV). The data is from a field survey and data mining models were used. The result showed that professional status (FNIESV or ANIESV) could be classified based on SMTT usage and years of professional experience. Adaptive boosting gave the best classification accuracy, specificity, and other evolution metrics.
... In recent years, several automated valuation methods (AVMs) have been proposed by adopting machine learning (Lin and Chen 2011;Ahn et al. 2012;Azimlu, Rahnamayan, and Makrehchi 2021) and deep learning techniques (Law, Paige, and Russell 2019;Ge et al. 2019) but ignoring spatially proximal real estate (Fu et al. 2014). Besides, several works have utilized artificial neural networks or big data approach (Ge et al. 2019;Law, Paige, and Russell 2019;Bin et al. 2019b;Lee, Kim, and Huh 2021) to appraise the real estate. However, most of the previous works ignored the spatio-temporal dependencies among real estate transactions. ...
Real estate appraisal is a crucial issue for urban applications, which aims to value the properties on the market. Traditional methods perform appraisal based on the domain knowledge, but suffer from the efforts of hand-crafted design. Recently, several methods have been developed to automatize the valuation process by taking the property trading transaction into account when estimating the property value. However, existing methods only consider the real estate itself, ignoring the relation between the properties. Moreover, naively aggregating the information of neighbors fails to model the relationships between the transactions. To tackle these limitations, we propose a novel Neighbor Relation Graph Learning Framework (ReGram) by incorporating the relation between target transaction and surrounding neighbors with the attention mechanism. To model the influence between communities, we integrate the environmental information and the past price of each transaction from other communities. Moreover, since the target transactions in different regions share some similarities and differences of characteristics, we introduce a dynamic adapter to model the different distributions of the target transactions based on the input-related kernel weights. Extensive experiments on the real-world dataset with various scenarios demonstrate that ReGram robustly outperforms the state-of-the-art methods. Furthermore, comprehensive ablation studies were conducted to examine the effectiveness of each component in ReGram.
... To achieve a robust estimate of a property's value, the LSTM network was also provided with photos from the neighborhood of the building. Bin et al. [3] took advantage of attention modules [26] and fused information from both textual data and satellite images in order to automatically predict property prices in Los Angeles. Using Crowdsourcing, Poursaeed et al. [21] built a dataset with luxury scores for different room types. ...
The assessment and valuation of real estate requires large datasets with real estate information. Unfortunately, real estate databases are usually sparse in practice, i.e., not for each property every important attribute is available. In this paper, we study the potential of predicting high-level real estate attributes from visual data, specifically from two visual modalities, namely indoor (interior) and outdoor (facade) photos. We design three models using different multimodal fusion strategies and evaluate them for three different use cases. Thereby, a particular challenge is to handle missing modalities. We evaluate different fusion strategies, present baselines for the different prediction tasks, and find that enriching the training data with additional incomplete samples can lead to an improvement in prediction accuracy. Furthermore, the fusion of information from indoor and outdoor photos results in a performance boost of up to 5% in Macro F1-score.
... For instance, J. Liu et al. discovered that although LSTM has an excellent performance in recognizing 3D human actions, not all action joints have a positive effect on training, and some action joints produce a great deal of interference with training, and they added an attention mechanism to the original LSTM model in order to selectively focus on useful action sequences with the aid of global contextual information joints [21]. The Attention mechanism can also be applied to real estate appraisal, as demonstrated by J. Bin et al. [22], who conducted a multimodal fusion appraisal of Los Angeles real estate based on attention and discovered that the appraisal model performed well after the introduction of the Attention mechanism. A. Vaswani et al. [23] proposed a self-attentive mechanism that can enhance the performance of the model, parallelize the computation, and significantly reduce the training time. ...
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. How to accurately evaluate its price has been a hot issue for research by major institutions. 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.
... e workload of the appraiser becomes very large, the error is large, and the evaluation efficiency is very low, which is not suitable for the trend of the development of the digital economy. To solve this problem, relatively speaking, the error results between the batch assessment and the actual transaction price are relatively satisfactory, so batch assessment has been widely used in China's real estate tax base assessment [5]. At present, there is a relatively mature real estate tax base assessment system with batch assessment as the core and tax assessment as the main purpose in the world [6]. ...
With the continuous development of China’s digital economy and the continuous heating of the real estate market, real estate tax base assessment occupies an important position in the real estate market. The purpose is to improve the work efficiency of relevant personnel of real estate tax base assessment, reduce workload pressure, and improve the evaluation level. Real estate tax base assessment and real estate appraisal are studied in detail, and the factors of the real estate tax base assessment index are analyzed. Different real estate tax base assessment methods are compared, and the difference and connection between different methods are explored. The theory of batch assessment of real estate tax base is analyzed in depth, and the procedures for batch assessment implementation are summarized. On this basis, a deep learning neural network (DLNN) theory is proposed, and a real estate tax base assessment model based on DLNN is constructed. The reliability, accuracy, and relative superiority of the model are analyzed in detail, and the model is used to test the sample data and analyze the error. The results reveal that the DLNN model has better data fit and good reliability. Compared with other algorithms, it has certain advantages and smaller error values. In the sample test, the test value is closer to the actual value, the error is controllable, and it has high accuracy. Through training, it shows that the DL model has an excellent performance in tax base assessment, can meet the requirements of efficient batch assessment, and is expected to achieve the goal of completing a huge workload in a limited time and improve work efficiency. The real estate tax base assessment model by DLNN can bring some help to the real estate finance and taxation work and provide a reference for the batch assessment of tax base in the real estate industry.
... Bin et.al. put forward an innovative multi-modal fusion method to incorporate the geographical presentation from street maps into the real estate appraisal model with a deep neural network [1]. An Attention-Based Modality-Gated Networks (AMGN) was put forth by Huang et.al. to exploit the interrelationship between the modalities of images and texts and extract the discriminative features for multimodal sentiment analysis [14]. ...
In the clothing industry, garment pattern design serves as a significant middle link between fashion design and manufacturing. With the advent of advanced multimedia techniques, like virtual reality, 3D modeling, and interactive design, this work has become more intuitive. However, it is still a tremendous knowledge-based work that relied on the experienced patternmakers’ know-how. For enterprises, it will take a long time to cultivate a patternmaker from an abecedarian to an expert. Moreover, while facing fierce competition in the market, enterprises still have to endure the pressures and risks led by the turnover of experienced patternmakers. In this context, we put forward a knowledge-supported garment pattern design approach by learning the experienced patternmakers’ knowledge based on fuzzy logic and artificial neural networks. Based on this approach, we created a knowledge-supported pattern design model, consisting of several sub-models following the garment styles, considering the properties of fabrics and fitting degree. The inputs of the model are the feature body dimensions, while the outputs, namely the pattern parameters, can be generated following the fabric and fitting degree selected. Through performance comparison with other models and the actual fitting test, the results revealed that the present approach was applicable. Our proposed approach can not only support the non-expert patternmakers or abecedarians to make decisions when developing the patterns by reducing the difficulties of patternmaking but help the enterprises to reduce the dependencies on the experts, hence promoting the efficiency and reducing risks.
... Therefore, a deep learning model with more than one dimension is necessary for housing price analysis. In some housing price models, two-dimensional neural networks are only applied in the part of the supplementary image features but are not used for structural, locational and neighborhood variables [12,[20][21][22][23]. Due to this limitation, these "half 1-dimensional and half 2-dimensional" models also have room for improvement. ...
... Similarly, the text, indoor pictures or street view images were utilized by some studies as additional features for housing price modeling. Zhou [40] used CNN and LSTM when analyzing the description text of houses, Zhao [23] used CNN when extracting the visual characteristics of the indoor pictures, Fu [21] and Bin [22] used CNN to extract the characteristics of street view images around the houses. In these studies, although two-dimensional networks were applied for the additional features (texts, street view images, etc.), they were still not applied to the structural, locational, and neighborhood variables, which are the vital factors of the housing prices. ...
... We also apply a dropout operation in the first fully connected layer that randomly disables the weights of some neurons and prevents model overfitting [19]. Since in recent studies the attention mechanism has been demonstrated effective for the deep learning of housing prices [12,22,24,64], we are inspired to wrap the first fully connected layer in our network with the attention block [22], which turns the raw features into attended features. There are many characteristics extracted by the convolutional layers before they come into the fully connected layers, and the attention mechanism helps the network to distinguish the important features that contributes to the output layer (the price), which are suitable for the gradient descent. ...
With the development of urbanization and the expansion of floating populations, rental housing has become an increasingly common living choice for many people, and housing rental prices have attracted great attention from individuals, enterprises and the government. The housing rental prices are principally estimated based on structural, locational and neighborhood variables, among which the relationships are complicated and can hardly be captured entirely by simple one-dimensional models; in addition, the influence of the geographic objects on the price may vary with the increase in their quantities. However, existing pricing models usually take those structural, locational and neighborhood variables as one-dimensional inputs into neural networks, and often neglect the aggregated effects of geographical objects, which may lead to fluctuating rental price estimations. Therefore, this paper proposes a rental housing price model based on the convolutional neural network (CNN) and the synthetic spatial density of points of interest (POIs). The CNN can efficiently extract the complex characteristics among the relevant variables of housing, and the two-dimensional locational and neighborhood variables, based on the synthetic spatial density, effectively reflect the aggregated effects of the urban facilities on rental housing prices, thereby improving the accuracy of the model. Taking Wuhan, China, as the study area, the proposed method achieves satisfactory and accurate rental price estimations (coefficient of determination (R2) = 0.9097, root mean square error (RMSE) = 3.5126) in comparison with other commonly used pricing models.
... We usually consult real estate websites or agents to find a reference for the price of a house before conducting the final transaction of buying or renting it. In addition, real estate valuation may indicate the economic situation or urban vibrancy of related regions [1]. Businesses are inclined to invest in a location by referring to an assessment of the relevant real estate market, and renters usually need to evaluate the cost of living and expenditures based on the rental house prices in a certain place to determine the positions of their jobs and lives. ...
... Wang J. [32] uses the neural networks based on synaptic memristor to predict housing prices. Some researchers have used street view images [1,9,38] or indoor pictures [39] to help improve deep learning for housing price models. The multisource data-fusion and attention mechanism utilized by Bin [9] has performed efficiently in property value assessment. ...
... Recent studies have proved that the attention mechanism can be effective for the neural networks of the housing price [1,2,9,55]. In our research, the house variables [x 1 , x 2 , . . . ...
Models for estimating urban rental house prices in the real estate market continue to pose a challenging problem due to the insufficiency of algorithms and comprehensive perspectives. Existing rental house price models based on either the geographically weighted regression (GWR) or deep-learning methods can hardly predict very satisfactory prices, since the rental house prices involve both complicated nonlinear characteristics and spatial heterogeneity. The linear-based GWR model cannot characterize the nonlinear complexity of rental house prices, while existing deep-learning methods cannot explicitly model the spatial heterogeneity. This paper proposes a fully connected neural network–geographically weighted regression (FCNN–GWR) model that combines deep learning with GWR and can handle both of the problems above. In addition, when calculating the geographical location of a house, we propose a set of locational and neighborhood variables based on the quantities of nearby points of interests (POIs). Compared with traditional locational and neighborhood variables, the proposed “quantity-based” locational and neighborhood variables can cover more geographic objects and reflect the locational characteristics of a house from a comprehensive geographical perspective. Taking four major Chinese cities (Wuhan, Nanjing, Beijing, and Xi’an) as study areas, we compare the proposed method with other commonly used methods, and this paper presents a more precise estimation model for rental house prices. The method proposed in this paper may serve as a useful reference for individuals and enterprises in their transactions relevant to rental houses, and for the government in terms of the policies and positions of public rental housing.
