1# RECORD e075c6fe2099973f79200c3fb2110ef7bfef767a2# TITLE: Application of the hedonic pricing model to real estate valuation in Hanoi, Vietnam3# YEAR: 2024.0 VENUE: World Journal of Advanced Research and Reviews DOI: 10.30574/wjarr.2024.24.2.34584# AUTHORS: Ho Thi5# PAGES: 8 CANDIDATE TABLE PAGES (1-based): [5, 6]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.789===== PAGE 1 (score 14, numeric-paren 0, table-words 1, var-words 12) =====1011 Corresponding author: Hoan Duong Thi 12Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. 13Application of the hedonic pricing model to real estate valuation in Hanoi, Vietnam 14Hoan Duong Thi * 15School of Economics, Hanoi University of Industry, 298 Cau Dien, Bac Tu Liem, Hanoi, Vietnam. 16World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 17Publication history: Received on 04 October 2024; revised on 15 November 2024; accepted on 18 November 2024 18Article DOI: https://doi.org/10.30574/wjarr.2024.24.2.3458 19Abstract 20The value of real estate is determined by the relationship between supply and demand in the market, location, and the 21physical structure of the land and surrounding area. During the development of infrastructure in the capital area of 22Hanoi, Vietnam, changes have occurred that directly impact housing prices. This study applies the Hedonic pricing 23model and asking prices to predict the market value of real estate and establish the correlation between the factors 24affecting land prices in Hanoi in 2023 and 2024. The results show that six main factors influence real estate prices: area, 25number of bedrooms, proximity to the city center, type, amenities, and structure. Among these, proximity to the city 26center has a negative impact, while the remaining factors have a positive impact on housing prices in this area. Based 27on these results, participants in the real estate market can make accurate predictions about housing prices, and market 28regulators can gain a comprehensive view of this market, particularly the factors influencing housing prices. 29Keywords: Real estate; Hedonic pricing model; Hanoi capital; Real estate market 301. Introduction31Across countries worldwide, a new and modern approach has emerged in determining real estate (RE) prices, involving 32mathematical tools to establish the relationship between attributes and RE prices. This approach has led to the 33development of the Hedonic pricing model, which uses regression models to quantify the impact of various factors on 34RE prices. Over the years, the Hedonic regression method has been widely applied in housing market research to assess 35the relationship between house prices and their characteristics. However, each RE has unique attributes under different 36conditions, making it impossible to establish an exhaustive list of attributes (O'Sullivan & Gibb, 2006). In developed 37industrial countries, housing is a significant component contributing to wealth and is also a major factor influencing 38household spending and savings (Case et al., 2004). Each house has its own attributes, such as transportation, amenities, 39structural features, neighborhood, and environment (Leong, 2003). When valuing a house, a set of inherent attributes, 40such as location, structure, or neighborhood, plays a crucial role, directly affecting the house price (Yoo & Kyriakidis, 412009). 42In Vietnam, real estate is defined as an asset that includes land, buildings, constructions, and other assets attached to 43the land as prescribed by law. Therefore, determining RE prices essentially involves determining the value of the land 44and the associated assets, generally referred to as house pricing in this study. The real estate market is one of the key 45markets of the economy, as it relates directly to a large volume of assets in terms of scale, nature, and multifaceted value 46in the national economy. The housing market is the most significant part of the RE market. 47Currently, methods for determining RE prices include the direct comparison method, the extraction method, the income 48method, the surplus method, and the coefficient-based land price adjustment method. These methods represent 49traditional approaches to RE valuation. The selection of a specific method depends on the intended use of the land 50parcel, the nature of the RE under consideration, the available asset data, market information about that asset type, and 515253===== PAGE 5 (score 26, numeric-paren 2, table-words 6, var-words 8) =====5455World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 561912 574. Results and Analysis 58First, the paper analyzes the correlation between the variables. The results are presented in Table 2 as follows: 59Table 2 Correlation Between Variables 60 61Area 62Dien 63Tich 64Bedrooms 65Bathrooms 66Centre 67Type 68Amenities 69Structure 70AREA 711.000 72 73 74 75 76 77 78 79DIENTICH 800.128 811.000 82 83 84 85 86 87 88BEDROOMS 890.776 900.146 911.000 92 93 94 95 96 97BATHROOMS 980.546 990.167 1000.656 1011.000 102 103 104 105 106CENTER 107-0.590 1080.010 109-0.473 110-0.369 1111.000 112 113 114 115TYPE 1160.595 117-0.046 1180.429 1190.315 120-0.300 1211.000 122 123 124AMENITIES 1250.776 1260.082 1270.471 1280.334 129-0.413 1300.615 1311.000 132 133STRUCTURE 1340.712 135-0.092 1360.536 1370.370 138-0.422 1390.435 1400.661 1411.000 142Table 2 shows that the distance from the house to the center (CENTER) is negatively correlated with its price (PRICE) 143while the other independent variables are positively related to the house price (PRICE). 144Table 3 Tests for Multicollinearity and Heteroskedasticity 145Multicollinearity Test 146Autocorrelation Test 147Variable 148VIF 149AREA 1502.52 151 152Prob>chi2 = 0.000*** 153DIENTICH 1542.36 155BEDROOMS 1562.18 157BATHROOMS 1581.79 159CENTER 1601.73 161 162TYPE 1631.41 164 165AMENITIES 1661.14 167 168Mean VIF = 1.77 169 170*** significance level at 1%. 171According to Table 3, the model is considered to have no serious multicollinearity issues. (Mean VIF = 1,77<10). 172From Table 4, the estimation results are significant at the 1% level (Prob > F = 0.000). Specifically, the independent 173variables account for 85.59% of the variability in housing prices. Thus, the independent variables explain 85.59% of the 174variation in the price variable, while the remaining 14.41% is explained by factors outside the model and random errors. 175It can be concluded that the model is well-fitting and has practical applicability. 176 177 178 179180181===== PAGE 6 (score 31, numeric-paren 8, table-words 3, var-words 14) =====182183World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 1841913 185Table 4 Estimation Results 186Dependent variable: House price (PRICE) 187Variables 188Coef. 189P>|z| 190Constant 19117.093*** 1920.000 193AREA 1940.001** 1950.023 196DIENTICH 1970.065*** 1980.000 199BEDROOMS 2000.003 2010.640 202BATHROOMS 203-0.020*** 2040.000 205CENTER 2060.148** 2070.036 208TYPE 2090.087*** 2100.000 211AMENITIES 2120.050*** 2130.000 214Number of obs 215195 216R-squared 21785.59% 218Significance level 219F(8, 196) = 276.08 220Prob>F = 0.000*** 221Specifically, CITY_CENTER has a negative impact (β = -0.020) on PRICE at the 1% significance level, while PRICE is 222positively influenced by AREA (β = 0.001, at the 5% significance level), BEDROOMS (β = 0.065, at the 1% significance 223level), TYPE (β = 0.148, at the 5% significance level), AMENITIES (β = 0.087, at the 1% significance level), and 224STRUCTURE (β = 0.050, at the 1% significance level). However, the study did not find a significant effect of BATHROOMS 225on PRICE. Therefore, the estimated model has the following equation: 226PRICE = 17.093 + 0.001 AREA + 0.065 BEDROOMS - 0.020 CITY_CENTER + 0.148 TYPE + 0.087 AMENITIES + 0.050 227STRUCTURE + ε 228Effect of house size (AREA) on housing price (PRICE): The results show a positive impact on PRICE. This indicates that 229houses with larger areas tend to have higher prices. This result is consistent with the findings of Yayar and Demir 230(2014). Accordingly, larger houses meet the needs of large families, providing more spacious and comfortable living 231spaces. These houses can also be used specifically for business or office purposes. 232Effect of the number of bedrooms (BEDROOMS) on housing price (PRICE): The results indicate that BEDROOMS 233positively affect PRICE. This is consistent with the findings of Selim (2009). Accordingly, houses with more bedrooms 234are suitable for families with multiple members. These houses also allow for hosting guests. 235Effect of house location (CITY_CENTER) on housing price (PRICE): The results indicate a negative effect of CITY_CENTER 236on PRICE. This suggests that houses farther from the city center may be sold at lower prices compared to those closer 237to the center. This is due to the difficulty residents face in commuting to work or downtown areas, as well as the limited 238availability of public amenities compared to the latter. This finding is consistent with the studies of Selim (2009) and 239Yayar and Demir (2014). 240Effect of house type (TYPE) on housing price (PRICE): The results show that PRICE is positively affected by TYPE. In 241densely populated urban areas, houses with street frontage or located on main roads have higher prices compared to 242those in alleys or narrow streets. This is consistent with Selim (2009)'s findings. 243Effect of local amenities (AMENITIES) on housing price (PRICE): The study finds a positive effect of amenities such as 244parks on PRICE. Houses near parks, plazas, schools, or shopping centers are more desirable due to their convenience 245for recreation, learning, or shopping, and are therefore sold at higher values. This finding is consistent with those of 246Cebula (2009) and Selim (2009). 247248249===== PAGE 7 (score 20, numeric-paren 0, table-words 0, var-words 20) =====250251World Journal of Advanced Research and Reviews, 2024, 24(02), 1908–1915 2521914 253Effect of house structure (STRUCTURE) on housing price (PRICE): The study finds that PRICE is positively affected by 254STRUCTURE. This result was also found in the analysis of Cebula (2009). Newly built houses with modern architecture 255and design are more desirable and fetch higher prices. 2565. Conclusion 257This study shows that, in Hanoi, Vietnam, proximity to the city center negatively affects housing prices. Meanwhile, 258houses with larger areas, modern structures, more bedrooms, gardens, and local amenities (such as plazas, parks, 259schools, and shopping centers) have higher values. Based on these findings, participants in the housing market can make 260accurate predictions about housing prices, and housing market regulators can gain a comprehensive view of the market, 261particularly regarding the factors affecting housing prices. This is essential and serves as a basis for proposing 262appropriate solutions to promote an effective and sustainable housing market. Furthermore, these findings can be 263widely applied by appraisers at low cost, while developers can highlight high-value houses that meet customer needs. 264For distributors, it facilitates the improvement of distribution channels, allowing their projects to be quickly and 265effectively introduced to customers. The results also allow buyers to easily determine the true value of a house. Despite 266achieving its objectives, the study has its own limitations as it does not analyze other determinants that may affect 267housing prices, such as macroeconomic factors, developer reputation, and the effectiveness of housing distribution 268channels. 269References 270[1] 271A., & Ismail, S. (2012). Multiple Regressions in Analysing House Price Variations. Communications of the IBIMA, 2721–9. 273[2] 274Amenyah, I.D., Fletcher, E.A. (2013). Factors determining residential rental prices. Asian Economic and Financial 275Review, 3(1), 39-50. 276[3] 277Aluko (2011). 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