# RECORD 1956ccee53765fb417ccdc3f95db189441def931 # TITLE: Analyzing the Impact of Accessibility on Residential Property Price: A Case study of Hanoi's Central Districts # YEAR: 2025.0 VENUE: Tạp chí Khoa học liên ngành và Nghệ thuật DOI: 10.64711/dq21w997 # AUTHORS: Le Minh Son; L. T. K. Ly; Trang Thu Doan # PAGES: 27 CANDIDATE TABLE PAGES (1-based): [8] # ABSTRACT: This research focuses on real estate values in central Hanoi, with the objective of analyzing factors influencing real estate prices, with particular emphasis on locational characteristics, for the case of Hanoi’s central districts. A geographically weighted regression (GW)R model is employed to address the limitations of traditional Hedonic Pricing Models (HPM), based on data from 1,545 properties across 12 central districts of Hanoi during the period from November 2024 to March 2025. The study evaluates the impact of 7 accessibility variables: distance to hospitals, parks, schools, universities, shopping centers, administrative offices at various levels, and the city center. Results show that the relationship between distance to public amenities and property prices varies significantly according to geographic location - a characteristic not reflected in traditional HPM models. Notably, many variables that lack statistical significance in the HPM model show differentiated impacts at specific locations in the GWR model, with higher coefficients of determination, demonstrating better explanatory power for real estate price variations. The research has demonstrated spatial heterogeneity in the relationships between valuation variables, indicating that location characteristics and spatial variation have not been adequately considered in current valuation methods in Vietnam. These findings not only address the limitations of HPM models but also provide important reference perspectives for policymakers regarding the significance of spatial factors. ===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) ===== Tạp chí Khoa học liên ngành và Nghệ thuật Tập 1, Số 2 (2025) 81 NGHIÊN CỨU – TRAO ĐỔI 1 School of Interdisciplinary Sciences and Arts, Vietnam National Univeristy, Hanoi * Email: sonminhle@vnu.edu.vn ABSTRACT This research focuses on real estate values in central Hanoi, aiming to analyze factors influencing real estate prices, with particular emphasis on locational characteristics, for the case of Hanoi’s central districts. A geographically weighted regression (GWR) model is employed to address the limitations of traditional Hedonic Pricing Models (HPM), based on data from 1,545 properties across 12 central districts of Hanoi during the period from November 2024 to March 2025. The study evaluates the impact of 7 accessibility variables: distance to hospitals, parks, schools, universities, shopping centers, administrative offices at various level, and the city center. Results show that the relationship between distance to public amenities and property prices varies significantly across geographic locations – a characteristic not reflected in traditional HPM models. Notably, many variables that lack statistical significance in the HPM model show differentiated impacts at specific locations in the GWR model, with higher coefficients of determination, demonstrating better explanatory power for real estate price variations. The research has demonstrated spatial heterogeneity in the relationships between valuation variables, indicating that location characteristics and spatial variation have not been adequately considered in current valuation methods in Vietnam. These findings not only address the limitations of HPM models but also provide important reference perspectives for policymakers regarding the significance of spatial factors. Keywords: Geographically weighted regression; GWR; hedonic; HPM; real estate; valuation; Hanoi. ANALYZING THE IMPACT OF ACCESSIBILITY ON RESIDENTIAL PROPERTY PRICE: A CASE STUDY OF HANOI’S CENTRAL DISTRICTS Le Minh Son 1*, Le Thi Hoa Ly 1, Doan Thu Trang 1 Lê Minh Sơn 1*, Lê Thị Hoa Ly 1, Đoàn Thu Trang 1 Received: 15 May 2025 Revised: 28 August 2025; Accepted: 10 September 2025 Ngày nhận bài: 15 tháng 5 năm 2025 Ngày nhận bài sửa: 28 tháng 8 năm 2025; Ngày duyệt đăng: 10 tháng 9 năm 2025 PHÂN TÍCH TÁC ĐỘNG CỦA KHẢ NĂNG TIẾP CẬN ĐẾN GIÁ BẤT ĐỘNG SẢN NHÀ Ở: NGHIÊN CỨU TRƯỜNG HỢP TẠI CÁC QUẬN TRUNG TÂM HÀ NỘI TÓM TẮT Nghiên cứu này tập trung vào giá trị bất động sản tại khu vực trung tâm Hà Nội, nhằm phân tích các yếu tố ảnh hưởng đến giá bất động sản, đặc biệt chú trọng đến đặc điểm vị trí đối với trường hợp các quận trung tâm của Hà Nội. Mô hình hồi quy có trọng số theo địa lý (GWR) được sử dụng để khắc phục những hạn chế của Mô hình Định giá Hedonic (HPM) truyền thống, dựa trên dữ liệu từ 1.545 bất động sản tại 12 quận trung tâm của Hà Nội trong giai đoạn từ tháng 11 năm 2024 đến tháng 3 năm 2025. Nghiên cứu đánh giá tác động của 7 biến số về khả năng tiếp cận: khoảng cách đến bệnh viện, công viên, trường học, trường đại học, trung tâm mua sắm, cơ quan hành chính các cấp và trung tâm thành phố. Kết quả cho thấy mối quan hệ giữa khoảng cách đến các tiện ích công cộng và giá bất động sản thay đổi ===== PAGE 8 (score 10, numeric-paren 6, table-words 1, var-words 2) ===== VNU Journal of Interdisciplinary Sciences and Arts Vol.1, No.2 (2025) 88 STUDIES – DISCUSSION In which, is the dependent variable – usually the monetary value of the property are dependent variables and are coefficients to be estimated in the model. Model 3.1 can be rewritten in the more general matrix form as followed: (3.2) In which, is the matrix of size nx1 containing the values of observed dependent variables. X is the matric of size n x (k+1) containing observed independent variables. is the matrix of size (k+1) x 1 containing variable coefficients and is matrix of size n x 1 with residual terms. In mathematical form, these matrices are written as: The estimation process of the impact of each independent variable in X on Y, i.e. estimation of values, is shown as: (3.3) And since these matrices are not of the same size, we multiply both sides with transposed matrix XT to achieve: (3.4) in formular 3.4 is the vector of coefficients in the model. In the conventional HPM approach, the value of these coefficients is global in the whole study area. The GWR extends this model by incorporating spatial factors into the model, to take into account local factors within the study area. First, for each observation point, the GWR model assigns spatial coordinates to the model: (3.5) Similarly, we can rewrite the general form of GWR model as: (3.6) where geographical coordinates are the assigned to each observation point . This allows the relationship between the dependent variable and the independent variables to vary from location to location. In matrix form, the GWR model is shown as: (3.7) Where is a matrix of coefficients that vary with geographic location, and ⊗ is a logical matrix multiplication (between two matrices of the same size): each value of is multiplied by the corresponding value of X. Matrix now take a new updated form: ===== PAGE 9 (score 18, numeric-paren 2, table-words 0, var-words 16) ===== Tạp chí Khoa học liên ngành và Nghệ thuật Tập 1, Số 2 (2025) 89 NGHIÊN CỨU – TRAO ĐỔI 6 For calculation of weightings applied, we used the default settings in ArcGIS to select bandwidth and applied the Akaike Information Criterion (AIC) for optimal calculation based on works by Fotheringham et al. (1998) and Bellefon & Floch (2018) which argued for AIC as a reliable and most efficient settings. 7 “SD” is short for “shortest distance”, with HOS, PRK, SCH, UNI, SHP, CENL, CEN correspondingly stand for Hospital, Park, School, University, shopping malls (shopping centers), Center (local) – the position of district level people’s committee buildings, and Center (Hanoi) – the position of Hanoi people’s committee building. The values in each row of is now estimated as: (3.8) Where the matrix is a diagonal matrix of size n x n, where is the weight assigned to the observation data point at location , based on coordinates This weight6 reflects the influence of observation point on point . The weight of a point close to will be higher than a point far from . The weight of a location decreases with increasing distance from . The fundamental difference between the HPM and the GWR model is shown in equations 3.4 and 3.8. In the conventional HPM, the estimated coefficients are spatially constant while in the GWR model, the estimated coefficients can vary spatially – expressed numerically as taking values within a range This spatial variation is adjusted through the weight matrix . The main results of the GWR analysis are usually presented in the form of multiple maps. Each independent variable requires at least one map to represent the variation of the estimated coefficients in space. 3.3. Model specification From the conventional HPM approach (Model 3.1) we first define the standard HPM with the following form: (3.9) Where PRICE is the advertised selling price of residential property (measured in million VND/m²), SDHOS, SDPRK, SDSCH, SDUNI, SDSHP are independent variables respectively represent the shortest distance as-the-crow-flies from real estate to the nearest urban services which are hospitals, parks and green spaces, schools, shopping centers, universities. SDCENL, SDCEN are independent variable representing shortest distance of real estate i to the nearest local people’s committee location and to the Hanoi People’s committee location7. is the error term and are coefficients to be estimated in the model. Access to these urban services – or amenities – (Figure 2), we argue, are basic and essential matter-of-fact need that everyday life requires to happen: medical facilities in case of sickness; education for themselves and family members; green spaces and shopping as utility enhancers; engagement with public institution for essential daily services and information. The selection of these variables align with other empirical works which include several variables to measure accessibility based on distance to different amenities such as by Wu et al. (2019) with variables representing distance to central business district, district center, high-speed rail, metro station, density, park, hospital, school. Wang et al. (2020) distinguished between distance to shopping mall, food market, supermarket