Influence of Accessibility to Urban Service Amenities on Housing Prices: Evidence from Beijing

Zhang, Chao; Xiong, Mimi; Wei, Xuehui

doi:10.1061/(asce)up.1943-5444.0000795

Cited by 10 publications

(4 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the sample size, the order of the variables, the correlation between the variables, and the degree of significance all affect how stepwise techniques perform, SVM does not perform very well when the data set has more noise and it is more suitable for classification problem when classes are clearly separated from one another. Due to the influence of a wide range of influential factors that affect real estate price valuation, policy adjustment [16], environmental events [17], accessibility to urban service amenities [18], and public transportation services and events [19]. However, Machine learning is less efficient than deep learning in complex and nonlinearity problems.…”

Section: Related Workmentioning

confidence: 99%

Real Estate Tax Prediction using Deep Neural Network and Bayesian Optimization

2023

IJIES

View full text Add to dashboard Cite

Real estate tax base assessment and evaluation systems are crucial for financing public services and digital economy transformation. To automate and unify complex tax procedures, an intelligent taxation model is needed. Deep learning neural network (DLNN) models have been limited for small-scale, high-dimensional data, but this paper creates an automated model using DLNN, offering superiority, interpretability, and dependability. The model offers lower error levels and high accuracy. The intelligent automated taxation model will improve real estate tax inspection offices efficiency by enabling precise tax base assessments and valuations. Deep learning techniques may enhance real estate price projections, resulting in more precise assessments of taxes. The findings reveal that, compared to other benchmark price predictors, the proposed model achieves greater accuracy (95%-99%) in different datasets and it has the potential to be generalized for real estate taxation authorities and tax inspection offices. The approach is helpful in automated real estate price prediction and taxation applications. This ground-breaking research study proposes a revolutionary strategy that employs deep learning neural network DLNN and challenges the conventional approaches to real estate tax base assessment.

show abstract

Section: Related Workmentioning

confidence: 99%

Real Estate Tax Prediction using Deep Neural Network and Bayesian Optimization

2023

IJIES

View full text Add to dashboard Cite

show abstract

“…The past research established the complexity of predicting the real-estate price value due to its influence from a wide range of influential determinates, such as inflation rate (Stukhart 1982;Poterba 1984;Qiao and Guo 2014), policy adjustment (Zheng and Yan 2017), environmental events (Yue et al 2020), accessibility (Zhang et al 2022) and urban (Zhang et al 2022) and public transportation services (Wen et al 2022). To date, several studies have explored artificial intelligence (AI) (Rafiei and Adeli 2016;Cao et al 2018;Gondia et al 2020) in a variety of construction and real-estate predictions applications.…”

Section: Research Backgroundmentioning

confidence: 99%

Real-estate price prediction with deep neural network and principal component analysis

Mostofi

Toğan

Başağa

2022

Organization, Technology and Management in Construction: An International Journal

View full text Add to dashboard Cite

Despite the wide application of deep neural networks (DNN) models, their application over small-sized real-estate price prediction is limited due to the reduced prediction accuracy and the high-dimensionality of the dataset. This study motivates small-sized real-estate agencies to take DNN-driven decisions using the available local dataset. To improve the high-dimensionality of real-estate price datasets and thus enhance the price-prediction accuracy of a DNN model, this paper adopts principal component analysis (PCA). The PCA benefits in improving the prediction accuracy of a DNN model are threefold: dimensionality reduction, dataset transformation and localisation of influential price features. The results indicate that, through the PCA-DNN model, the transformed dataset achieves higher accuracy (90%–95%) and better generalisation ability compared with other benchmark price predictors. The spatial and building age proved to have the most impact in determining the overall real-estate price. The application of PCA not only reduces the high-dimensionality of the dataset but also enhances the quality of the encoded feature attributes. The model is beneficial in real-estate and construction applications, where the absence of medium and big datasets decreases the price-prediction accuracy.

show abstract

“…Income determines the housing costs that can be afforded, so it is an important factor in choosing a place to live [17,18]. Moreover, numerous studies have shown that housing costs determine the accessibility to living amenities [19][20][21][22]. A typical example is the residential area near the Central Business District (CBD).…”

Section: Introductionmentioning

confidence: 99%

Exploring Urban Amenity Accessibility Within Residential Segregation: Evidence from Seoul's Apartment Housing

Lee,

Jeong,

2024

Preprint

View full text Add to dashboard Cite

Residential segregation refers to the phenomenon where people of different socioeconomic backgrounds live in spatially separated areas. It is essential to ensure equitable access to urban amenities for all residents in pursuit of the normative values in urban planning. To achieve this planning goal, the disparity in accessibility to urban amenities needs to be appropriately diagnosed. Private apartments and public rental apartments are representative types of residences where residential segregation is likely to occur in the context of South Korea, since these two types show considerable differences in the education, income, and occupations. The objective of this study is to develop an analysis framework for diagnosing the difference in accessibility to urban amenities between the two residential types, and to empirically demonstrate their utility in the planning process. The empirical analysis conducted on Seoul revealed that not only were there significant local variations in accessibility between the two segregated residential areas, but the overall differences across the entire area were also pronounced. The proposed framework is useful in supporting decision-making processes for locating new public facilities or identifying regional priorities for guiding the placement of private amenities, with the aim of mitigating differences between segregated residential areas.

show abstract

Influence of Accessibility to Urban Service Amenities on Housing Prices: Evidence from Beijing

Cited by 10 publications

References 70 publications

Real Estate Tax Prediction using Deep Neural Network and Bayesian Optimization

Real Estate Tax Prediction using Deep Neural Network and Bayesian Optimization

Real-estate price prediction with deep neural network and principal component analysis

Exploring Urban Amenity Accessibility Within Residential Segregation: Evidence from Seoul's Apartment Housing

Contact Info

Product

Resources

About