1# RECORD 7de41ea5f998e11b3cc730af7771c0a4b73baa2f2# TITLE: Approach to hedonic model and analyzing factors affecting real estate prices in Hanoi3# YEAR: 2022.0 VENUE: International Journal of Statistics and Applied Mathematics DOI: 10.22271/maths.2022.v7.i4b.8494# AUTHORS: Nhat Hong Nguyen; Quang Duy Phung5# PAGES: 4 CANDIDATE TABLE PAGES (1-based): [3]6# ABSTRACT: In real estate valuation techniques and housing market research, real estate prices are often analyzed using The Hedonic model based on factors that affect real estate prices. The Hedonic model shows the factors that affect real estate prices. The determinants of real estate prices in Hanoi in this article use 240 observations. The variables that have the greatest influence on real estate prices are the location of the property, the area of the land, the area of the house, the distance from the property to the city center, and the distance from the property to the street frontage.789===== PAGE 1 (score 14, numeric-paren 0, table-words 1, var-words 12) =====1011 12~101~ 13International Journal of Statistics and Applied Mathematics 2022; 7(4): 101-104 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29ISSN: 2456-1452 30Maths 2022; 7(4): 101-104 31© 2022 Stats & Maths 32www.mathsjournal.com 33Received: 01-04-2022 34Accepted: 04-05-2022 35 36Nguyen Hong Nhat 37National Economic University, 38Hanoi, Vietnam 39 40Phung Duy Quang 41Foreign Trade University, 42Hanoi, Vietnam 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86Corresponding Author: 87Phung Duy Quang 88Foreign Trade University, 89Hanoi, Vietnam 90 91 92 93 94 95 96 97 98 99 100Approach to hedonic model and analyzing factors 101affecting real estate prices in Hanoi 102 103Nguyen Hong Nhat and Phung Duy Quang 104 105DOI: https://doi.org/10.22271/maths.2022.v7.i4b.849 106 107Abstract 108In real estate valuation techniques and housing market research, real estate prices are often analyzed 109using The Hedonic model based on factors that affect real estate prices. The Hedonic model shows the 110factors that affect real estate prices. The determinants of real estate prices in Hanoi in this article use 240 111observations. The variables that have the greatest influence on real estate prices are the location of the 112property, the area of the land, the area of the house, the distance from the property to the city center, and 113the distance from the property to the street frontage. 114 115Keywords: Real estate price, Hedonic model, land area, house area, distance 116 1171. Introduction 118For many households, housing not only provides them shelter, but it is also a major asset. Real 119estate is one of the most important assets in the investment and buying and selling portfolio of 120households. Indeed, in industrialized countries, real estate is the asset that makes up the largest 121share of affluent households. As a result, the value of their apartments has a great impact on 122consumption, household savings and the supply-demand mechanism. Thus, home prices are of 123great concern to property developers, banks, policy makers or the general public as well as 124existing and potential owners (Ustaoilu, E. 2003) [10]. 125The real estate market can be influenced by macroeconomic variables, spatial differences, 126community structural features and properties of real estate. Real estate valuation is necessary 127to provide a quantitative measure of the benefits and liabilities accrued from ownership of real 128estate. Valuations are performed by real estate agents, appraisal and inspection specialists, 129mortgage lenders, brokers, property developers, investors and fund managers, lending 130companies, market researchers, analysts, consultants and other professionals. 131 1322. The Hedonic model and experimental studies 133The Hedonic model is one of the oldest techniques of economic valuation developed by 134Lancaster (1966), Ridker (1967) [7], Grilihces (1971), Rosen (1974) and others. Originally used 135to study the relationship between air pollution reductions and property values, the Hedonic 136model became an important study in the second half of the 1970s and throughout the 1980s. 137During this period, the Hedonic pricing method has become a very important tool for academic 138research, used in theoretical and experimental studies to determine the monetary value of 139goods in relation to the environmental and physical characteristics of the property. 140The first basic Hedonic model was presented by Ridker (1967) [7]: 141 142𝑃𝑖= 𝑓(𝑆1𝑖, . . . , 𝑆𝑘𝑖; 𝑁1𝑖, . . . , 𝑁𝑚𝑖, 𝑍1𝑖, 𝑍2𝑖, . . . . , 𝑍𝑛𝑖) 143 144In which: Pi is the house price; S is the structural characteristics of the house (1 2 … k) such as 145the area of the house, the number of rooms, the type of construction and some other factors. 146N is neighborhood characteristics (1….m), such as distance to workplace, quality of school, 147local crime rate and other factors. 148149150===== PAGE 3 (score 96, numeric-paren 63, table-words 6, var-words 5) =====151152 153~103~ 154International Journal of Statistics and Applied Mathematics 155https://www.mathsjournal.com 156 157Table 1: Description of variables 158 159Variable 160Description 161Expectation of Sign 162LogPRICE 163The logarithm of real estate prices 164 165AREA 166Land area (m2) 167+ 168HAREA 169House area (m2) 170+ 171DIS1: 172Distance from the property to the center (km) 173- 174DIS2: 175Distance from the property to street frontage (km) 176- 177WIDTH 178Front pavement width (m) 179+ 180SHAPE 181Real estate shape: dummy variable (gets the value 1 if the back is wider or equal to the front of the land; 182value 0 if the back is narrower than the front) 183+ 184NOF 185Number of floors: dummy variable (gets the value 1 if the number of floors <= 4; value 0 if > 4) 186- 187TERRACE 188Terrace: dummy variable (gets value 1 if there is a terrace; value 0 if there is no terrace) 189+ 190LOCATION 191Location: dummy variable (gets value 1 if property is in frontage; value 0 if property is in alleys) 192+ 193USE 194Usage: dummy variable (gets value 1 if the property is for business, office, or residential use; gets value 0 195for residential use only) 196+ 197 198Table 2: Descriptive statistics of variables 199 200Variable 201Average value 202Maximum value 203Minimum value 204Standard deviation 205LogPRICE 2068.20 20712.65 2087.24 2090.84 210AREA 21156.50 212260.60 21352.10 21424.3 215HAREA 2162.780 2174.30 2182.12 2191.23 220DIS1: 22124.60 22230.30 22322.40 2244.62 225DIS2: 2264.60 2276.60 2283.20 2291.26 230WIDTH 2316.30 2328.65 2335.40 2341.68 235SHAPE 2360.42 2371.00 2380.00 2390.12 240NOF 2414.20 2421.00 2430.00 2440.36 245TERRACE 2460.65 2471.00 2480.00 2490.21 250LOCATION 2510.43 2521.00 2530.00 2540.36 255USE 2560.54 2571.00 2580.00 2590.42 260 261Table 3: Results of 6 regression models 262 263Variable 264Model 1 265Model 2 266Model 3 267Model 4 268Model 5 269Model 6 270AREA 2710.0142*** 272(9.426) 2730.0143*** 274(9.426) 2750.0145*** 276(9.864) 2770.0141*** 278(10.124) 2790,.0148*** 280(10.012) 2810,.0146*** 282(9.942) 283HAREA 2840.0008 285(1.432) 2860.0008 287(1.420) 2880.0008 289(1.274) 2900.0009* 291(2.524) 2920,.0012* 293(2.788) 2940.0012* 295(2.982) 296DIS1: 297-0.045* 298(-1.984) 299-0.065* 300(-1.928) 301-0.0665* 302(-1.948) 303-0.0648* 304(-2.114) 305-0.0684* 306(-2.682) 307-0.0840* 308(-2.683) 309DIS2: 310-0.0016* 311(-1.914) 312-0.0016* 313(-1.976) 314-0.0016* 315(-1.978) 316-0.0016* 317(-1.928) 318-0.0017* 319(-1.926) 320-0.0015* 321(-1.968) 322LOCATION 3230.9284*** 324(7.192) 3250.8294*** 326(7.899) 3270.8368*** 328(9.827) 3290.8426*** 330(9.248) 3310.8424*** 332(9.842) 3330.8468*** 334(9.468) 335WIDTH 336-0.0085* 337(-1.826) 338-0.0088* 339(-1.824) 340-0.0086* 341(-1.946) 342-0.0084* 343(-1.964) 344-0.0094* 345(-1.848) 346 347SHAPE 3480.1283 349(0.984) 3500.1078 351(0.872) 3520.1268 353(0.924) 3540.1046 355(0.924) 356 357 358NOF 359-0.1193 360(-0.742) 361-0.1428 362(-0.846) 363-0.1464 364(-0.846) 365 366 367 368TERRACE 369-0.0426 370(-0.492) 371-0.0428 372(-0.548) 373 374 375 376 377USE 3780.0602 379(0.620) 380 381 382 383 384 385R2 3860.7824 3870.7918 3880.7824 3890.7784 3900.7689 3910.7846 392R2 correction 3930.7739 3940.7846 3950.7758 3960.7727 3970.7640 3980.7809 399Statistical value F 40091.8873*** 401109.8138*** 402119.1681*** 403136.4073*** 404155.7097*** 405213.9984*** 406Robust standard errors in parentheses 407*** p<0.001, ** p<0.05, * p<0.1 408Source: Estimated results from the model 409 4104. Result 411Consider model 1 412LogPRICE = 12.45 + 0.0142AREA + 0.0008HAREA −0.045DIS1 −0.0016DIS2 413 414+0.9284LOCATION −0.0085WIDTH + 0.1283SHAP −0.1193NOF- 0.0426TERRACE + 0.0602USE + ε 415(1) 416 417Model 1 has multicollinearity between variables and autocorrelation between USE and LOCATION, because the correlation 418coefficient between these two variables is 0.78. At the same time, the USE variable was not statistically significant, so the USE 419variable was excluded from model 1. 420421422===== PAGE 4 (score 10, numeric-paren 0, table-words 0, var-words 7) =====423424 425~104~ 426International Journal of Statistics and Applied Mathematics 427https://www.mathsjournal.com 428 429Consider model 2 430LogPRICE = 12.45 + 0.0143AREA + 0.0008HAREA −0.0605DIS1 −0.0016DIS2 431 432+0.8294LOCATION −0.0088WIDTH + 0.1078SHAP −0.1428NOF- 0.0428TERRACE + ε 433(2) 434 435This model has eliminated autocorrelation but the test still shows signs of collinearity because 𝑅𝑖4362 is high. Hence model 2’s 437variables will be omitted. The TERRACE variable has low statistical significance and the sign is opposite to our expectation, so 438we remove the variable TERRACE from model 2. 439Similarly, when considering model 3, 4, we also remove variables NOF, SHAPE from the model. 440 441Consider model 3 442LogPRICE = 12.45 + 0.0148AREA + 0.0012HAREA −0.0684DIS1 −0.0017DIS2 443 444+0.8424LOCATION −0.0094WIDTH + ε 445(3) 446 447This model has eliminated autocorrelation but the test still shows collinearity because 𝑅𝑖4482 is high. Hence model 5’s variables will 449be omitted. The WIDTH variable has low statistical significance and the opposite sign is desired, so we remove the TERRACE 450variable from model 5. 451 452Consider model 4 453LogPRICE = 9.45 + 0.0146AREA + 0.0012HAREA −0.0840DIS1 −0.0015DIS2 454 455+0.8468LOCATION + ε 456(4) 457 458The variables of model 6 are all statistically significant, the signs of the coefficients are consistent with the expectations. The 459analysis results show that the price of real estate in the frontage is usually 84.68% higher than in alleys. For real estate in the alley, 4601m further from the frontage causes the average price to decrease by 0.12%. Similarly, if real estate is 1km away from the center, 461the price will decrease by 8.4% on average. If the land area increases by 1m2, the price will increase by 1.46%. 462We illustrate the following two cases: 463*Case 1: Real estate in the frontage LOCATION =1; DIS1= 0; AREA =60m2; HAREA = 186.2m2; DIS2=3.2 receives lnPRICE = 46411.39144. Then PRICE = 88.56 (equivalent to 86 billion 560 million VND). With a frontage house 3.2 km from the center, with 465an area of 60m2, the house area is 186.2m2 (4 floors), the price is 86 billion 560 million VND. 466*Case 2: real estate in alleys. LOCATION =0; DIS1= 0; AREA =60m2; HAREA = 186.2m2; DIS2=3.2 receives lnPRICE = 46710.54464. Then PRICE = 37.973 (equivalent to 37 billion 973 million). With houses in alleys 3.2 km from the center, with an area 468of 60m2, the house area is 186.2m2 (4 floors), the price has decreased significantly, at only 37 billion 973 million VND. 469 4705. Conclusion 471This article applies Hedonic model and the Status - Quality Trade-off Theory to determine real estate prices in Hanoi area. The 472results show that location variables, distance from property to street frontage, distance from property to center, land area and 473house area are important variables affecting property prices. 474 4756. References 4761. Gabriel Kayode Babawale. The Impact of Neighbourhood Churches on House Prices, Journal of Sustainable Development. 4773/2011;4(1):246-253. 4782. Hasan Selim. Determinants of house prices in Turkey: Hedonic regression versus artificial neural network, Expert Systems 479with Applications. 2009;36:2843-2852. 4803. Hoàng Hữu Phê, Patrick Wakely. Vị thế, chất lượng và sự lựa chọn khác: Tiến tới một lý thuyết mới về vị trí dân cư đô thị, 481tạp chí Đô thị học, xuất bản tại Vương quốc Anh. 1/2000;37:1. 4824. Kim. North Versus South: The Impact of Social Norms in the Market Pricing of Private Property Rights in Vietnam, World 483Development. 2007;35(12):2079-2095. 4845. Nguyễn Mạnh Hùng, Trần Văn Trọng, Lý Hưng Thành, Trần Thanh Hùng, Hoàng Hữu Phê, Phương pháp định giá BĐS ứng 485dụng thuyết vị thế - chất lượng, trang 1-8. 4866. Nguyễn Trọng Hoài, Phùng Thanh Bình, Nguyễn Khánh Duy. Dự báo và phân tích dữ liệu trong kinh tế và tài chính, NXB 487Thống kê, 2009. 4887. Ridker Ronald G, John Henning A. The Determinants of Residential Property Values with Special Reference to Air Pollution, 489The Review of Economics and Statistics. 1967;49(2):246-57. 4908. Sérgio AB, et al. Economics of Air Pollution: Hedonic Price Model and Smell Consequences of Sewage Treatment Plants in 491Urban Areas, Economics of Air Pollution. 8/2002;234:1-25. 4929. Sibel Selim. Determinants of house prices in Turkey: Hedonic regression model, Doðuþ niversitesi Dergisi, 2008, 65-76. 49310. Ustaoðlu E. Hedonic Price Analysis of Office Rents: A Case Study of the Office Market in Ankara, Middle East Technical 494University, Unpublished MSc, Thesis, 2003. 495 496