# RECORD e075c6fe2099973f79200c3fb2110ef7bfef767a # TITLE: Application of the hedonic pricing model to real estate valuation in Hanoi, Vietnam # YEAR: 2024.0 VENUE: World Journal of Advanced Research and Reviews DOI: 10.30574/wjarr.2024.24.2.3458 # AUTHORS: Ho Thi # PAGES: 8 CANDIDATE TABLE PAGES (1-based): [5, 6] # ABSTRACT: The value of real estate is determined by the relationship between supply and demand in the market, location, and the physical structure of the land and surrounding area. During the development of infrastructure in the capital area of Hanoi, Vietnam, changes have occurred that directly impact housing prices. This study applies the Hedonic pricing model and asking prices to predict the market value of real estate and establish the correlation between the factors affecting land prices in Hanoi in 2023 and 2024. The results show that six main factors influence real estate prices: area, number of bedrooms, proximity to the city center, type, amenities, and structure. Among these, proximity to the city center has a negative impact, while the remaining factors have a positive impact on housing prices in this area. Based on these results, participants in the real estate market can make accurate predictions about housing prices, and market regulators can gain a comprehensive view of this market, particularly the factors influencing housing prices. ===== PAGE 1 (score 14, numeric-paren 0, table-words 1, var-words 12) =====  Corresponding author: Hoan Duong Thi Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Application of the hedonic pricing model to real estate valuation in Hanoi, Vietnam Hoan Duong Thi * School of Economics, Hanoi University of Industry, 298 Cau Dien, Bac Tu Liem, Hanoi, Vietnam. World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 Publication history: Received on 04 October 2024; revised on 15 November 2024; accepted on 18 November 2024 Article DOI: https://doi.org/10.30574/wjarr.2024.24.2.3458 Abstract The value of real estate is determined by the relationship between supply and demand in the market, location, and the physical structure of the land and surrounding area. During the development of infrastructure in the capital area of Hanoi, Vietnam, changes have occurred that directly impact housing prices. This study applies the Hedonic pricing model and asking prices to predict the market value of real estate and establish the correlation between the factors affecting land prices in Hanoi in 2023 and 2024. The results show that six main factors influence real estate prices: area, number of bedrooms, proximity to the city center, type, amenities, and structure. Among these, proximity to the city center has a negative impact, while the remaining factors have a positive impact on housing prices in this area. Based on these results, participants in the real estate market can make accurate predictions about housing prices, and market regulators can gain a comprehensive view of this market, particularly the factors influencing housing prices. Keywords: Real estate; Hedonic pricing model; Hanoi capital; Real estate market 1. Introduction Across countries worldwide, a new and modern approach has emerged in determining real estate (RE) prices, involving mathematical tools to establish the relationship between attributes and RE prices. This approach has led to the development of the Hedonic pricing model, which uses regression models to quantify the impact of various factors on RE prices. Over the years, the Hedonic regression method has been widely applied in housing market research to assess the relationship between house prices and their characteristics. However, each RE has unique attributes under different conditions, making it impossible to establish an exhaustive list of attributes (O'Sullivan & Gibb, 2006). In developed industrial countries, housing is a significant component contributing to wealth and is also a major factor influencing household spending and savings (Case et al., 2004). Each house has its own attributes, such as transportation, amenities, structural features, neighborhood, and environment (Leong, 2003). When valuing a house, a set of inherent attributes, such as location, structure, or neighborhood, plays a crucial role, directly affecting the house price (Yoo & Kyriakidis, 2009). In Vietnam, real estate is defined as an asset that includes land, buildings, constructions, and other assets attached to the land as prescribed by law. Therefore, determining RE prices essentially involves determining the value of the land and the associated assets, generally referred to as house pricing in this study. The real estate market is one of the key markets of the economy, as it relates directly to a large volume of assets in terms of scale, nature, and multifaceted value in the national economy. The housing market is the most significant part of the RE market. Currently, methods for determining RE prices include the direct comparison method, the extraction method, the income method, the surplus method, and the coefficient-based land price adjustment method. These methods represent traditional approaches to RE valuation. The selection of a specific method depends on the intended use of the land parcel, the nature of the RE under consideration, the available asset data, market information about that asset type, and ===== PAGE 5 (score 26, numeric-paren 2, table-words 6, var-words 8) ===== World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 1912 4. Results and Analysis First, the paper analyzes the correlation between the variables. The results are presented in Table 2 as follows: Table 2 Correlation Between Variables Area Dien Tich Bedrooms Bathrooms Centre Type Amenities Structure AREA 1.000 DIENTICH 0.128 1.000 BEDROOMS 0.776 0.146 1.000 BATHROOMS 0.546 0.167 0.656 1.000 CENTER -0.590 0.010 -0.473 -0.369 1.000 TYPE 0.595 -0.046 0.429 0.315 -0.300 1.000 AMENITIES 0.776 0.082 0.471 0.334 -0.413 0.615 1.000 STRUCTURE 0.712 -0.092 0.536 0.370 -0.422 0.435 0.661 1.000 Table 2 shows that the distance from the house to the center (CENTER) is negatively correlated with its price (PRICE) while the other independent variables are positively related to the house price (PRICE). Table 3 Tests for Multicollinearity and Heteroskedasticity Multicollinearity Test Autocorrelation Test Variable VIF AREA 2.52 Prob>chi2 = 0.000*** DIENTICH 2.36 BEDROOMS 2.18 BATHROOMS 1.79 CENTER 1.73 TYPE 1.41 AMENITIES 1.14 Mean VIF = 1.77 *** significance level at 1%. According to Table 3, the model is considered to have no serious multicollinearity issues. (Mean VIF = 1,77<10). From Table 4, the estimation results are significant at the 1% level (Prob > F = 0.000). Specifically, the independent variables account for 85.59% of the variability in housing prices. Thus, the independent variables explain 85.59% of the variation in the price variable, while the remaining 14.41% is explained by factors outside the model and random errors. It can be concluded that the model is well-fitting and has practical applicability. ===== PAGE 6 (score 31, numeric-paren 8, table-words 3, var-words 14) ===== World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 1913 Table 4 Estimation Results Dependent variable: House price (PRICE) Variables Coef. P>|z| Constant 17.093*** 0.000 AREA 0.001** 0.023 DIENTICH 0.065*** 0.000 BEDROOMS 0.003 0.640 BATHROOMS -0.020*** 0.000 CENTER 0.148** 0.036 TYPE 0.087*** 0.000 AMENITIES 0.050*** 0.000 Number of obs 195 R-squared 85.59% Significance level F(8, 196) = 276.08 Prob>F = 0.000*** Specifically, CITY_CENTER has a negative impact (β = -0.020) on PRICE at the 1% significance level, while PRICE is positively influenced by AREA (β = 0.001, at the 5% significance level), BEDROOMS (β = 0.065, at the 1% significance level), TYPE (β = 0.148, at the 5% significance level), AMENITIES (β = 0.087, at the 1% significance level), and STRUCTURE (β = 0.050, at the 1% significance level). However, the study did not find a significant effect of BATHROOMS on PRICE. Therefore, the estimated model has the following equation: PRICE = 17.093 + 0.001 AREA + 0.065 BEDROOMS - 0.020 CITY_CENTER + 0.148 TYPE + 0.087 AMENITIES + 0.050 STRUCTURE + ε Effect of house size (AREA) on housing price (PRICE): The results show a positive impact on PRICE. This indicates that houses with larger areas tend to have higher prices. This result is consistent with the findings of Yayar and Demir (2014). Accordingly, larger houses meet the needs of large families, providing more spacious and comfortable living spaces. These houses can also be used specifically for business or office purposes. Effect of the number of bedrooms (BEDROOMS) on housing price (PRICE): The results indicate that BEDROOMS positively affect PRICE. This is consistent with the findings of Selim (2009). Accordingly, houses with more bedrooms are suitable for families with multiple members. These houses also allow for hosting guests. Effect of house location (CITY_CENTER) on housing price (PRICE): The results indicate a negative effect of CITY_CENTER on PRICE. This suggests that houses farther from the city center may be sold at lower prices compared to those closer to the center. This is due to the difficulty residents face in commuting to work or downtown areas, as well as the limited availability of public amenities compared to the latter. This finding is consistent with the studies of Selim (2009) and Yayar and Demir (2014). Effect of house type (TYPE) on housing price (PRICE): The results show that PRICE is positively affected by TYPE. In densely populated urban areas, houses with street frontage or located on main roads have higher prices compared to those in alleys or narrow streets. This is consistent with Selim (2009)'s findings. Effect of local amenities (AMENITIES) on housing price (PRICE): The study finds a positive effect of amenities such as parks on PRICE. Houses near parks, plazas, schools, or shopping centers are more desirable due to their convenience for recreation, learning, or shopping, and are therefore sold at higher values. This finding is consistent with those of Cebula (2009) and Selim (2009). ===== PAGE 7 (score 20, numeric-paren 0, table-words 0, var-words 20) ===== World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 1914 Effect of house structure (STRUCTURE) on housing price (PRICE): The study finds that PRICE is positively affected by STRUCTURE. This result was also found in the analysis of Cebula (2009). Newly built houses with modern architecture and design are more desirable and fetch higher prices. 5. Conclusion This study shows that, in Hanoi, Vietnam, proximity to the city center negatively affects housing prices. Meanwhile, houses with larger areas, modern structures, more bedrooms, gardens, and local amenities (such as plazas, parks, schools, and shopping centers) have higher values. Based on these findings, participants in the housing market can make accurate predictions about housing prices, and housing market regulators can gain a comprehensive view of the market, particularly regarding the factors affecting housing prices. This is essential and serves as a basis for proposing appropriate solutions to promote an effective and sustainable housing market. Furthermore, these findings can be widely applied by appraisers at low cost, while developers can highlight high-value houses that meet customer needs. For distributors, it facilitates the improvement of distribution channels, allowing their projects to be quickly and effectively introduced to customers. The results also allow buyers to easily determine the true value of a house. 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