1# RECORD 3b820e938ca4b455736f08f285e1b5bb43f7674a2# TITLE: Determinants of Residential Real Estate Property Prices in Ethiopia: The Case of Bahir Dar City3# YEAR: 2024.0 VENUE: International Journal of Business and Economics Research DOI: 10.11648/j.ijber.20241303.114# AUTHORS: Tamirat Mekuria; Seid Yimam; Yohans Negashi5# PAGES: 18 CANDIDATE TABLE PAGES (1-based): [14, 15]6# ABSTRACT: The Ethiopian real estate industry has grown in the previous two decades, especially in the capital city of the country and regional capitals, including Bahir Dar. Real estate prices have doubled and even tripled in a few years because of different factors. Thus, the purpose of this study is to analyse the factors affecting residential real estate prices in Ethiopia, particularly in Bahir Dar City. For this purpose, the study used both purposive and stratified random sampling techniques. Descriptive and inferential statistics were used to analyze the data, and the ordinary least squares method was used to identify the factors that influence residential real estate prices. The hedonic regression model result reveals that R<sup>2</sup> statistics (0.880) indicate that 88% of the variation in residential real estate prices can be explained by variation in the independent variables. Hedonic regression model results revealed that plot size, floor area, number of rooms, age of the house, external wall finishing material, ceiling finishing material, the direction of the house, distance of the property from the main road, and involvement of brokers in the transaction process, were major micro-factors that had a significant effect on residential real estate prices at the 5% level of significance. Furthermore, the interview results show that the challenges faced by real estate developers mostly include loans and aid issues, high-interest rates, mortgage restrictions, the financial capacity of real estate companies, delays in plan approvals, and rising costs of construction materials are among the major challenges affecting investment in real estate. The study suggests that the municipality of Bahir Dar City should provide an adequate supply of land to real estate developers, improve infrastructure development, and federal and regional governments should provide the credit lending mechanism by lowering interest rate especially for low-income class.78910===== PAGE 1 (score 18, numeric-paren 2, table-words 1, var-words 14) =====1112International Journal of Business and Economics Research 132024, Vol. 13, No. 3, pp. 46-63 14https://doi.org/10.11648/j.ijber.20241303.11 15 16 17*Corresponding author: 18 19Received: 14 April 2024; Accepted: 6 May 2024; Published: 24 May 2024 20 21Copyright: © The Author(s), 2024. Published by Science Publishing Group. This is an Open Access article, distributed 22under the terms of the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/), which 23permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. 24 25Research Article 26Determinants of Residential Real Estate Property Prices in 27Ethiopia: The Case of Bahir Dar City 28Tamirat Mekonnen Mekuria1, * , Seid Hussen Yimam2 , 29Yohans Teshome Negashi3 30 311Department of Real Property Valuation, Institute of Land Administration, Oda Bultum University, Chiro, Ethiopia 322Department of Real Property Valuation, Institute of Land Administration, Bahir Dar University, Bahir Dar, Ethiopia 333Department of Real Property Valuation, College of Business and Economics, Dire Dawa University, Dire Dawa, 34Ethiopia 35 36Abstract 37The Ethiopian real estate industry has grown in the previous two decades, especially in the capital city of the country and 38regional capitals, including Bahir Dar. Real estate prices have doubled and even tripled in a few years because of different 39factors. Thus, the purpose of this study is to analyse the factors affecting residential real estate prices in Ethiopia, particularly 40in Bahir Dar City. For this purpose, the study used both purposive and stratified random sampling techniques. Descriptive and 41inferential statistics were used to analyze the data, and the ordinary least squares method was used to identify the factors that 42influence residential real estate prices. The hedonic regression model result reveals that R2 statistics (0.880) indicate that 88% 43of the variation in residential real estate prices can be explained by variation in the independent variables. Hedonic regression 44model results revealed that plot size, floor area, number of rooms, age of the house, external wall finishing material, ceiling 45finishing material, the direction of the house, distance of the property from the main road, and involvement of brokers in the 46transaction process, were major micro-factors that had a significant effect on residential real estate prices at the 5% level of 47significance. Furthermore, the interview results show that the challenges faced by real estate developers mostly include loans 48and aid issues, high-interest rates, mortgage restrictions, the financial capacity of real estate companies, delays in plan 49approvals, and rising costs of construction materials are among the major challenges affecting investment in real estate. The 50study suggests that the municipality of Bahir Dar City should provide an adequate supply of land to real estate developers, 51improve infrastructure development, and federal and regional governments should provide the credit lending mechanism by 52lowering interest rate especially for low-income class. 53Keywords 54Hedonic Regression Model, Micro-Factors, Property Prices, Residential Real Estate 55 56 57 58 596061===== PAGE 14 (score 43, numeric-paren 0, table-words 15, var-words 11) =====6263International Journal of Business and Economics Research 64http://www.sciencepg.com/journal/ijber 65 6659 67Coefficientsa 68Model 69Unstandardized Coeffi-70cients 71Standardized 72Coefficients 73T 74Sig. 75B 76Std. Error 77Beta 78Broker involvement in the transaction process 79.468 80.202 81.126 822.320 83.023* 84External wall finishing material 85.383 86.183 87.138 882.089 89.040* 90Floor finishing material 91.035 92.025 93.083 941.391 95.168 96Ceiling finishing material 97.023 98.023 99.054 1001.008 101.013* 102Direction of the house (East=1 Non-East=0) 103.456 104.160 105.150 106.285 107.036* 108Land grade 109-.009 110.011 111-.048 112-.865 113.389 114Accessible road type 115-.001 116.008 117-.005 118-.094 119.925 120Log_ Floor Area in square meter 121.601 122.175 123.307 1243.441 125.001* 126Log_ Gross plot size in square meter 127.335 128.110 129.258 1303.052 131.003* 132Log_ Age of the House in years 133-.187 134.054 135-.250 136-3.434 137.001* 138 139Log_ Number of rooms 140.148 141.066 142.131 1432.240 144.028* 145 146Log_ Distance of the property from the main 147road in meter 148-.097 149.031 150-.191 151-3.115 152.002* 153Dependent Variable: Log_ Residential Real Estate Price 154* Statistically significant at 0.05 155Source: Regression model result, 2024 156Table 9 shows the value of each coefficient. This model 157confirmed that structural factors (building characteristics), 158age of the property, gross plot size, floor area, number of 159rooms, external wall finishing material, ceiling finishing ma-160terial, and direction of the house are statistically significant 161in affecting the price of residential real estate. Also, based on 162locational attributes, the distance from the property to the 163main road is statistically significant. In addition to these fac-164tors, the brokers’ involvement in the transaction is statistical-165ly significant. Table 9 shows that the coefficients of these 166variables have a p-value of less than 0.05. 167The hedonic regression model shows variables that were 168significant in the overall model, that is, age of the property (t 169= -3.434, p-value = 0.001), gross plot area (plot size) (t = 1703.052, p-value = 0.003), distance of the property from the 171main road (t = -3.115, p-value = 0.002), a broker involved in 172the transaction (t = 2.320, p-value = 0.023), ceiling finishing 173material (t = 1.008, p-value = 0.013), the direction of the 174house (t = 0.285, p-value = 0.036), external wall finishing 175material (t = 2.089, p-value = 0.040), and a number of rooms 176(t = 2.240, p-value = 0.028). The results of the regression 177model indicate that the prices of residential real estate are 178influenced by building characteristics. In addition, these bro-179kers’ involvement in the transaction also influenced residen-180tial real estate prices. However, the land grade, accessible 181road type, and floor finishing material, are not significant 182variables. The p-value of those variables is greater than 5%, 183so these variables have no significant effect on the sales price 184of residential real estate properties. 185Therefore, from the above Table 9, 14.539 is the intercept 186of this linear relationship, which indicates if the independent 187variables were all rated as zero, residential real estate prices 188rising would be 14.539. According to the regression result, 189the age of the property and distance of the property from the 190main road has a negative relationship with the sales price of 191residential real estate. According to different literatures, old-192er houses are often associated with a lower house price com-193pared to newer houses. In the current study, we found that 194the age of a house has a significant effect on the price of res-195idential real estate. The sign of building age is negative, indi-196cating that the older the house is, the lower the house price. 197This study's results show that a relatively new house with 198good quality has a higher price than an old or low-quality 199house. A 1-year increase in the age of the house leads to a 200decrease in the price of residential real estate by (18.7%), 201ceteris paribus. 202The property near the main road has a higher price than 203the property far from the main road. The property is far from 204the main road; at a distance of one meter, the sales price of 205the residential real estate will decrease by (9.7%). The re-206207208===== PAGE 15 (score 28, numeric-paren 0, table-words 8, var-words 10) =====209210International Journal of Business and Economics Research 211http://www.sciencepg.com/journal/ijber 212 21360 214gression result reveals that keeping other explanatory varia-215bles constant, an addition of 1 square meter to the plot size 216leads to a change in the price of residential real estate by 217(33.5%). This result is consistent with the findings of 218(Guntermann et al., 2015, and Gu, 2018), that residential 219properties with a larger plot size have a higher price than 220those with a smaller plot size. The OLS regression suggests 221that with an addition of floor size by 1 square meter, the 222price of residential real estate increases by (60.1%). As the 223number of rooms increased by one room, the price of resi-224dential real estate increased by (14.8%). 225Many experts agree that real estate brokers play an im-226portant role in the real estate industry. However, their find-227ings demonstrate that even if real estate agents play a signifi-228cant role in the market by contacting buyers and sellers, they 229may monopolize the market and take advantage of clients, 230especially in areas where there are flaws in the market. Simi-231lar findings were obtained from this study in Bahir Dar City, 232where the majority of real estate deals involve brokers, who 233do a fantastic job of connecting the two sides. Brokers, how-234ever, charge more than they did and have a significant im-235pact on price increases. This occurs when there is an infor-236mation asymmetry, and as a result, both parties are asked to 237pay more for their perceived advantages. This is in line with 238the findings of the study by Marete (2011), who also claims 239that home buyers who acquired their property through an 240agent or broker are subjected to additional demands by form-241ing a conspiracy with one or both participants. According to 242this study, there will be a 46.8% change in the sales price of 243residential real estate with broker engagement. 244The material from which the ceiling finishing material of 245the building is made also determines the price of residential 246real estate. Many studies support the idea that real estate with 247better quality and a long-lasting ceiling-finishing material 248has better value. As depicted in Table 9, those properties 249finished through gypsum and painting has a better selling 250price than those properties finished through plastic. The ex-251pected price change in residential real estate with ceilings 252finished with gypsum is increased by 2.3% compared to plas-253tic ceiling finishing material types. This result is consistent 254with the finding of [3], which states that the construction 255material, which directly affects the cost of construction, has a 256significant effect on the price of real estate. The price of res-257idential real estate with quartz paint external wall finishes 258increase by 38.3% compared to plaster with cement external 259wall types. 260The direction of the house has a statistically significant ef-261fect on the residential real estate price. According to the re-262gression results, the house located in the east direction has a 263higher price difference than the house located in the non-east 264direction. As depicted in coefficient Table 9, those buildings 265located in the east direction have a better selling price than 266those located in the non-east direction. The expected price 267difference in residential real estate located in the east aspect 268increases by 45.6% compared to residential real estate locat-269ed in the non-east aspect. 270The amount to which each variable influences the price of 271residential real estate in the area is shown by the standard 272coefficient (Beta) value of the explanatory (independent) 273variable. It is presented in Table 9 above. It gives a picture of 274how influential or important the independent factors are 275about the price of residential real estate. The influence of the 276variable increases with beta magnitude. So, based on the 277analysis's findings, the brokers’ role in the deal, the direction 278of the house, and floor area have the greatest impact on the 279residential real estate property price in the study area. 280From the above Table 9, therefore, the model is thus as in-281dicated below; 282𝐿𝑜𝑔−𝑅𝑅𝐸𝑃= 14.593 + 0.468(𝐵𝑅) + 0.383(𝐸𝑊𝐹𝑀) + 0.023(𝐶𝐹𝑀) + 0.456(𝐷𝐻) + 0.601(𝐿𝑜𝑔−𝐹𝐿𝐴)283+ 0.335(𝐿𝑜𝑔−𝐺𝑃𝐴) −0.187(𝐿𝑜𝑔−𝐴𝑔𝑒 ) + 0.148(𝐿𝑜𝑔−𝑁𝑅) −0.097(𝐿𝑜𝑔−𝐷𝑀𝑅) + 𝑒𝑟𝑟𝑜𝑟𝑠 284Table 10. Summary of Hypothesis Testing. 285S.N 286Hypothesis 287Sig 288(P-Value) 289Level of Signif-290icance 291Conclusion 2921 293H1: There is significant relationship between age of the house and prices of 294residential real estate property 2950.001 2960.05 297Accepted 2982 299H2: There is significant relationship between number of rooms and prices of 300residential real estate property 3010.028 3020.05 303Accepted 3043 305H3: There is significant relationship between floor area and prices of residen-306tial real estate property 3070.001 3080.05 309Accepted 3104 311H4: There is significant relationship between proximity to the main road and 312prices of residential real estate property 3130.002 3140.05 315Accepted 3165 317H5: There is significant relationship between gross plot area and prices of resi-3180.003 3190.05 320Accepted 321322323===== PAGE 16 (score 15, numeric-paren 0, table-words 1, var-words 12) =====324325International Journal of Business and Economics Research 326http://www.sciencepg.com/journal/ijber 327 32861 329S.N 330Hypothesis 331Sig 332(P-Value) 333Level of Signif-334icance 335Conclusion 336dential real estate property 3376 338H6: There is significant relationship between broker involvement in the trans-339action and prices of residential real estate property 3400.023 3410.05 342Accepted 3437 344H7: There is significant relationship between external wall finishing materials 345and prices of residential real estate property 3460.040 3470.05 348Accepted 3498 350H8: There is significant relationship between floor finishing materials and pric-351es of residential real estate property 3520.168 3530.05 354Reject 3559 356H9: There is significant relationship between ceiling finishing materials and 357prices of residential real estate property 3580.013 3590.05 360Accepted 36110 362H10: There is significant relationship between direction of the house and prices 363of residential real estate property 3640.036 3650.05 366Accepted 36711 368H11: There is significant relationship between land grade and prices of residen-369tial real estate property 3700.389 3710.05 372Reject 37312 374H12: There is significant relationship between accessible road type and prices 375of residential real estate property 3760.925 3770.05 378Reject 379Source: Researcher, 2024 3805. Conclusion and Recommendations 381Conclusions 382The framework of price determinants for the research area 383was also developed using 12 variables that were discovered 384and categorized into the known residential real estate price 385determinants. To determine the price of residential real estate 386in the study area of Bahir Dar City, information on the varia-387bles taken into account was requested from property owners, 388real estate developers, and brokers. 389Findings from the correlation analysis result showed the 390price of residential real estate has a positive relationship with 391the ten explanatory variables: gross plot area, floor area, 392number of rooms, external wall finishing materials, floor 393finishing materials, ceiling finishing materials, the direction 394of the house, brokers involvement in the transaction process, 395land grade, and accessible road type. It was found that the 396price of residential real estate has a negative relationship 397with the age of the house, and its proximity to the main road. 398According to the findings of the regression model result, 399gross plot size, floor area, number of rooms, age of the house, 400proximity to the main road, the direction of the houses, ex-401ternal wall finishing material, ceiling finishing materials, and 402broker involved in the transaction process were identified as 403statistically significant factors in residential real estate prices 404at 5% level of significance. 405In general, the coefficients of the regression results show 406that factors like gross plot area, floor area, number of rooms, 407age of the house, distance from the property to the main road, 408the direction of the house, external wall finishing material, 409ceiling finishing materials, and brokers’ involvement in the 410transaction process have major influence on the prices of 411residential real estate. 412Recommendations 413This study has identified the significant factors that affect 414the price of residential real estate properties, and describe the 415challenges that real estate developers confront in Bahir Dar 416City. Thus, the study has forwarded the following recom-417mendations in light of conclusions that have been presented 418thus far: 4191) The real estate developers should invest in quality finishes 420and analyze the target market to understand the size of 421plots and the size of houses they most likely prefer. 4222) This study suggests that the government should concen-423trate on providing sufficient land to real estate develop-424ers to restrict the increase in property prices and make 425housing affordable for the middle and lower-income 426classes in the city. 4273) The paper also urges governments to actively pursue 428data collection and analysis of housing market trends. 429The information available was quite limited but crucial 430for making policy decisions, particularly in court. 4314) Since the current real estate prices seem to exclusively 432target the upper class and the diaspora, the government 433should develop a system, such as a price index for resi-434dential real estate sectors that would allow it to monitor 435the real estate price increase. 4365) The government should improve infrastructure develop-437ments that will improve the real estate sector's capacity to 438develop affordable residential real estate for their clients. 4396) Finally, the government should controlling high cost of 440construction brought about by acquisition cost of both 441