1. Introduction
Public facilities such as public transport, parks, hospitals, schools, and lake can be convenient for residents and can play an important role in the housing market (Chin and Foong 2006; Li and Wang 2010). In particular, urban green space has significant cultural and ecological value.
The green space provides multiple benefits, including aesthetic enjoyment, recreational opportunities, and ecological services (Cho et al. 2006; G´omez et al. 2010; Maimaitiyiming et al. 2014). It also benefits human health by providing a location for outdoor exercises and for releasing pollutants (Maller et al. 2006; Sander and Polasky 2009). Thus, renters and homebuyers are willing to pay more for houses adjacent to urban landscapes. However, the
International Journal of Social Science Research (IJSSR) eISSN: 2710-6276 | Vol. 5 No. 1 [March 2023]
Journal website: http://myjms.mohe.gov.my/index.php/ijssr
THE EFFECTS OF ACCESSIBILITY TO PUBLIC FACILITIES ON HOUSING PRICES IN PHNOM PENH
Kimhuy L. Y.1*, Vanda Chhiev2, Yat Yen3 and Veng Kheang Phun4
1 2 3 4 Department of Transport and Infrastructure Engineering, Institute of Technology of Cambodia, Phnom
Penh, CAMBODIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 29 January 2023 Revised date : 18 February 2023 Accepted date : 3 March 2023 Published date : 7 March 2023 To cite this document:
Kimhuy, L. Y., Chhiev, V., Yen, Y., &
Phun, V. K. (2023).THE EFFECTS OF ACCESSIBILITY TO PUBLIC FACILITIES ON HOUSING PRICES IN PHNOM PENH. International Journal of Social Science Research, 5(1), 174-184.
Abstract: This paper examines the effects of accessibility to public facilities on housing prices. Data on characteristics of 150 houses on sales in Phnom Penh 2021-2022 were gathered from a real estate agency, while proximity to public facilities (e.g., central business district, park, airport entrance) were computed based on geolocation in QGIS environment. The data were analysed using Hedonic regression models, in order to capture the effects of proximity between houses and public facilities on housing prices. It was found that the housing prices increased by approximately 21.6%, 17.0%, and 2.7% for houses locating 1 km closer to the CBD, parks, and airport entrance (p < 0.05), certeris paribus. The closer proximity to a public bus stop was also found to increase housing price, but insignificant (p
> 0.05). The findings from this study provide insights into policy discussion on how improved urban green space and accessibility to public transport services would benefit both citizens and property values.
Keywords: public facilities, hedonic regression model, urban green space, public transport, property value.
amenity values provided by green space are usually difficult to assess and quantify because they are intangible and cannot be easily priced, especially in the residential housing market (Jim and Chen 2006; Liu and Hite 2013).
Urban public transport and housing prices remains very confusing. Generally speaking, the accessibility of transport facilities has a positive effect impact on housing prices, because it makes it easier for residents to go to work or shop. Voith (1993) analysed the relationship between the rapid rail transit system and housing prices in Boston and Philadelphia, and found that the price of houses near public transport facilities in Boston was significantly higher by 6.7%, while in Philadelphia it was 7.5 to 8% higher. Benjamin and Sirmans (1996) studied the impact of the Washington subway system on housing prices and found that for every 0.1 miles away from subway stations, the corresponding property rents fell by 2.4% to 2.6%. Wang et al. (2015) study the impact of bus stop on housing price in central Cardiff between 2000 and 2009 found that the premium of 0.3% for every bus stop within 0.5 km. (Phun and Chalermpong, 2009) The premium of being near a transit station was greatest, with a premium of 1.8% for every 1 km nearer the station. The premium of locating near the airport is approximately 0.97% for every 1 km nearer to the airport.
The objective of this paper is to examine the effects of proximity to public facilities on housing prices in Phnom Penh. Particularly, we seek to understand the relationship between local residential property values and proximity to urban public facilities, including parks, schools, hospital, banks, shopping, and residential service facilities, as well as access to bus stops, train stations and airport. The findings are expected to provides some insights into consideration for improving urban amenities and public transport system, benefit to both citizens and housing market.
This study divided into five main sections. Following this introduction, literature review about previous study related to public facilities on housing price, methods of this study including the data collected, results and discussion and the final section is conclusion.
2. Literature Review
2.1 Public Facilities in Phnom Penh
Urban facilities can be defined as components of the city whose primary function is to provide public goods and services, either wholly or partly by government (DeVerteuil, 2000; Teitz, 1968). Urban facilities play a crucial role in the quality of people's everyday lives, and can be classified into historical amenities and relatively modern amenities, which are generated mainly by past and present government decisions regarding investment in education, medical care, transportation, and other infrastructure (Brueckner, Thisse, & Zenou, 1999; Li et al., 2016).
Infrastructure: Infrastructure includes public roads, bridges, highways and electricity. The government is proposing various initiatives and programs to put this infrastructure in place.
The provision of this basic infrastructure ensures the safe movement of people and materials in all regions of the country. Phnom Penh has come a short way in infrastructure development, but much work remains to be done as some remote areas of the country are still not connected to other developed regions.
Sanitation: Sanitation is an important facility that must be planned to create a cleaner environment. Some of the major sanitation facilities include proper disposal of waste, public toilets, purification of chemicals from industries, and hazardous waste management.
Public transport: Good public transportation is an essential facility that allows citizens of the country to travel locally and across the country faster, safer and more affordable. Public transport includes railways, buses, airlines, etc. The connectivity of the different regions of the country between them is taken into consideration when planning public transport. Railways and buses have not good connectivity yet in Phnom Penh. However, air connectivity remains a problem for some regions but slowly the situation is improving.
Health care: Health is one of the most important public facilities that must be provided by the government. Planning and implementation of government medical facilities such as hospitals, health centres and affordable drugs should be carried out in all regions of the country. In Phnom Penh, the government has set up civilian hospitals and medical centres in various parts of the city, which provide basic services to advance medical aid to citizens.
Water: Water is an essential facility. First, clean water should be available in public water treatment facilities. The canal should be constructed to make water available for agricultural use. Industries also use water as a material for many processes. In addition to providing water, the government should also provide facilities to purify domestic water and treatment facilities to ensure that harmful particles are removed from water from industries.
2.2 Public Transport Facilities and Hedonic Modelling
Investigating the relationship between public transportation infrastructure and residential property value has been a focus of research for more than five decades. Alonso (1964) first proposed the bid rent theory, which provides a theoretical basis for the study of how transport infrastructure and residential property values are related, in 1964. Since then, a broad range of case studies have been conducted on various locations to investigate whether and how the provision of road, rail, bus and other transportation services impact the housing market. Due especially to the punctuality, speed and low emissions of rail transit systems, the land and residential property values around rail stations has been found repeatedly to significantly increase, particularly near the entrance and exit points to the station. For example, Pan (2013) investigated residential property values near the Houston metro rail transit line in Texas.
Results show that the opening of the rail transit line generated a significant premium to the local residential property values. Zhang et al. (2016) also demonstrated that residential values in general increased for every kilometre of new rail track constructed in a metropolitan area.
Murat Celik and Yankaya (2006), Tian (2006), Zhang et al. (2007), Mulley et al. (2018), Hopkins (2018), and Wen et al. (2018) have all reached similar conclusions regarding the impact of transport infrastructure projects on increasing the value of residential property.
However, other studies have found the exact opposite impact.
Research into the impact of other forms of transport infrastructure have also varied in their conclusions. A study by Shen et al. (2017) concluded that TOD-impacted properties around bus stations in Seattle, Washington, were significantly impacted positively, with the greatest impact within 0.5 km of the stations. Deng et al. (2016) conducted a case study of the Bus Rapid Transit system in Beijing, and found that surrounding property values increase the closer the property is to a station. Efthymiou and Antoniou (2013) conducted a comparative study of
different kinds of transport facility. That study indicates that metro, tram, suburban rail, and bus stations have a positive impact on property value, where national rail stations, airports, and ports have negative impacts on residential property value. Further variation in the impact of transport infrastructure on property values, depending on local conditions, have been reported for end of ride bicycle facilities Welch et al. (2016), and for road and highway construction Martínez and Viegas (2009), Seo et al. (2018).
Proximity to the CBD, schools, bus stops, metro stations, retail stores, and urban parks has a positive effect on housing prices (Jang & Kang, 2015; Jim & Chen, 2007; Schläpfer et al., 2015; Schwartz et al., 2014; Xu et al., 2015; Zhang & Dong, 2018), compensated by the lower cost of commuting to various facilities (Tse, 2002). However, the literature on the effects of urban facilities on property values is mixed in terms of the magnitude and direction of the impact (Debrezion, Pels, & Rietveld, 2007; Tian et al., 2017), due largely to various econometric tools and frameworks used (Brasington & Haurin, 2009; Nguyen-Hoang &
Yinger, 2011). In addition, the proxies for accessibility generally only consider a limited range of externalities of urban facilities and do not consider location-specific attributes such as differences in the type, quality, and scarcity of urban facilities, which limits their usefulness in measuring the comprehensive effects of urban facilities (Clapp et al., 2008; Feng & Lu, 2013;
Jang & Kang, 2015).
In sum, the housing prices were reported to be influenced by three main factors such as location, structural and neighbourhood (Chau & Chin., 2003). Kohlhase (1991) found that the significance of structural attributes can change over time, and may vary between nations.
Numerous studies have revealed that the number of rooms and bedrooms (Fletcher, et al. 2000;
Li & Brown 1980), the number of bathrooms (Garrod & Willis 1992; Linneman 1980), and the floor area (Carroll, Clauretie, & Jensen 1996; Rodriguez & Sirmans 1994) are positively related to the sale price of houses. Kain and Quigley’s (1970) study further demonstrated that higher income households with more education prefer to live in relatively high-quality dwelling units located further away from the CBD.
3. Methodology 3.1 Data
There are several data sources required to calculate the impact of the public facilities on property values. First of all, 21 Century real estate company provided the data for property sales in Phnom Penh from 2021 to 2022. The data set includes sale prices and property characteristics of 150 houses. The sale price of the properties was expressed in united states dollar (USD). See table 2 for descriptive statistics.
Table 1: Definitions of Variables
Catalogue Variable Description
Dependent variable Housing price This variable represents sale price of each houses in USD
Characteristics of house
Floor area This variable represents the size of the house in square meter
Number of bedrooms This variable presents the total of bedrooms
Characteristics of
neighborhood CBD This variable represents the distance from house to central business district
(Wat Phnom)
Market This variable is measuring the distance from house to nearest traditional market Bus Stop This variable is measuring the distance
from house to nearest city bus station Train Station
This variable is measuring the distance from house to the train station entrance (Main station)
Airport This variable is measuring the distance from house to airport entrance
Characteristics of environment park
This variable is measuring the distance from house to nearest public park in Phnom Penh
Floor area. The floor area in this data, from 47 m2 to 388 m2 with an average of 209.18 m2. An analysis of the price per square meter shows that a large house will often be more expensive than a smaller house, which is typically because of the ostentation of a luxury house. Because the number of rooms and structure of the house is similar throughout Phnom Penh, only the area of a house is used in this study. Hence, the data for houses with areas that exceed 388 m2 are excluded to avoid bias.
Distance to Centre Business District (Wat Phnom). Wat Phnom is located north of Road 102, Norodom Boulevard, in the centre of Phnom Penh. We chose Wat Phnom as CBD and the distance from each property was calculated by Qgis3.26.3 (Network Analysis) instead of straight line (Euclidean).
Distance to nearest market. We collect Traditional Markets like Old Market (Phsar Chas) is a local market that is not at all geared to the tourist. It carries such items as fruits and vegetable, second hand clothes, hardware, motorcycle parts and religious items. Located on the river at the south end of the Old French Quarter. The distance is analysis from each property to nearest traditional local markets based on actual street network.
Distance to nearest parks. The public parks in Phnom Penh is collected in this study and the nearest distance is calculated in Qgis3.26.3. for example, The Royal Palace Park is located on the opposite side of the road to Sisowath Quay, which runs alongside the Tonle Sap River as passes through Phnom Penh city centre.
Distance to the nearest bus stop. Phnom Penh City Bus is a municipal public transport system that serves local people in Phnom Penh, the capital of Cambodia. The system opened to the public in September 2014 with 3 lines. Currently, there are 13 city bus routes in Phnom Penh.
The distance between house and bus stop is measured by network analysis in Qgis3.26.3.
Distance to train station entrance. The Phnom Penh main station in located next to the University of Health Science and we measured distance from each property to train station entrance.
Distance to airport entrance. The Phnom Penh international airport is located in the Pou Senchey District, 10 kilometres west of Phnom Penh, the nation's capital. The distance from each property to airport entrance is measured by actual network distance in Qgis3.26.3.
Regarding property characteristics, structural control variables consist of land area in square meter, floor area in square meter, house age, number of floors, number of bedrooms and number of bathrooms.
In addition to the structural and sales characteristics, location characteristics in the data sets, which are provided by the street address of property, are also utilized for hedonic modelling.
The addresses were coded into Geographic Information System (GIS) map for further spatial analysis using the software QGIS (3.26.3). Based on the GIS map of property locations, additional location-related variables were generated, such as distance to CBD (Wat Phnom), nearest market, park and distance to transportation facilities (i.e., distance to the nearest bus stop, train station, airport).
Figure 1: Map of the Study Area and Location Observations
Table 2: Model Variables and Descriptive Statistics
3.2 Hedonic Model
Hedonic property price models are widely used to estimate the contribution of different attributes (structural, neighbourhood, and environmental characteristics) to the value of a property as measured by its sale price or assessed value (Freeman, 2003).
In this study, we used the semi-log model for the statistical analysis. The semi-log specification is popular in many studies, thus allowing comparability of results and can be explained results as the percentage (Phun and Chalermpong, 2009).
This allows the interpretation of regression coefficients as the percentage change due to marginal increase in the value of variables. Because this function form has been used by many researchers in the past, our choice of specification enables us to compare our estimation results with those in many previous studies. The hedonic model of property prices can be written as follows:
ln saleprice = α + Xβ + Sγ + Nπ + ε (1) where
ln saleprice = natural log of property price X = vector of structural variables, S = vector of location variables, N = vector of environmental variables, α, β, γ, π = parameters to be estimated, and
ε = error term.
4. Results and Discussion
Various models with different specifications, by combining different set of available variables, were tested and run. The OLS estimation results of the best hedonic regression model with robust standard errors are shown in Table 3 As can be seen, the estimated model fits the data quite well, with adjusted R-squared of 0.6685, suggesting that 66.85% of variation in property prices could be explained by model. The coefficients of property characteristics variables are all significant at the 5 percent level (p < 0.05) and show correct signs.
Variable Mean Standard
Deviation Minimum Maximum
Dependent variable
Sale price (US$) 289433.3 200404.7 58000 899000
Floor area (m2) 209.186 74.916 47 388
Number of bedrooms 4.06 1.925 1 11
CBD (Wat Phnom) (km) 6.942 4.211 0.826 19.749
Distance to nearest Traditional Market (km) 1.844 1.954 0.066 12.739
Distance to nearest Public Park (km) 6.248 4.221 0.273 19.749
Distance to nearest Bus stop (km) 0.880 1.738 0.005 18.483
Distance to Train station (km) 6.418 4.101 0.600 19.858
Distance to Airport (km) 10.188 4.647 0.530 27.962
The coefficient of floor area and number of bedrooms are positive, and the magnitudes of these coefficients suggest that a marginal increase in the value of these variables would increase in the property price by 0.22% and 4.84%, respectively. Generally, Larger floor area houses are usually more expensive to rent than smaller ones, so the price of a house can also be higher.
Larger floor area houses can be used as office space or rental buildings, hotels and guesthouses, etc., and talk about a house with a large number of bedrooms is always a special feature for or those who buy a house because it is convenient for family members than share bedroom. It is reasonable that house with higher floor area and number of bedrooms the price is higher than the house with small floor area.
It is also shown in table 2 that the coefficients for location variables are highly significant, except for the coefficient of distance from each property to CBD (Wat Phnom) is negatively significant (p<0.05), suggesting that the property value increase by 21.59 % for every 1 km closer to CBD. There are many reasons why Central Business District (Wat Phnom) of Phnom Penh makes house prices rise because Wat Phnom is not only the centre of Phnom Penh, but also a natural park rich in large trees and historical sites that attract national and international tourists, in the centre of Phnom Penh is rich in high quality hospitals, schools and libraries with well access to public transport. As we expected the distance to central business district is attract premium price for property value because it has more job opportunities, commercial area, shopping centre and better street environments. Chen, Jie, and Qianjin Hao (2008) results shows that housing price on average drops 5% as the zone is one kilometre further away from the CBD.
Table 3: Results of Semi-Log Regression with Robust Standard Errors for ln(saleprice)
The coefficient of distance to nearest market is highly positive significant (p<0.01), suggesting that property value increase by 11.82% for every 1km further from the market. The reason why the price of houses near the traditional market is decreasing price because of poor waste
Regression with Robust Standard Error
Dependent Variable = ln (saleprice)
Number of observations = 150 F (8,141) = 62.50 Prob > F = 0.0000 R-squared = 0.6685 Root MSE = 0.40288
Coef.
Robust Std.
Err. t P > t
[95% confident interval]
Structural variables
Floor area (m2) 0.00220 0.00052 4.220 0.000 0.00117
0.00323
Number of bedrooms 0.04844 0.02236 2.170 0.032 0.00423 0.09265
Location variables: distance to (km)
CBD (Wat Phnom) -0.21594 0.08857 -2.440 0.016 -0.39103 -0.04085
Nearest traditional market 0.11822 0.02722 4.340 0.000 0.06441 0.17202 Nearest public park -0.17019 0.05259 -3.240 0.002 -0.27415 -0.06623
Nearest bus stop -0.02674 0.02302 -1.160 0.247 -0.07224 0.01876
Train station entrance 0.23954 0.10636 2.250 0.026 0.02928 0.44979 Airport entrance -0.02736 0.01243 -2.200 0.029 -0.05193 -0.00280
Constant 12.80233 0.17314 73.940 0.000 12.46004 13.14461
management, bad smell, which is one of the factors that affect the price of houses around the market, and another reason is because the market area is heavily travelled, it will cause traffic congestion, which in turn causes air pollution. These results are reasonable because house nearby the market is not comfortable for private single-family home.
The coefficient of distance nearest bus stop is not significant (p>0.05). The reason is local people do not pay much attention on public bus and public bus advertising is still limited. The coefficient of train station is positive significant, suggesting that the distance from each property to train station entrance increase by 23.95% for every 1km further from train station.
Some of the reasons why the price of a house near the train station is so cheap is because, first of all, when a train passes by, it closes the traffic, causing congestion and wasting time for people living in the area near the railway. For other factors are due to the noise of the train engine and the smoke emitted by the train engine, which also affects the air quality of the nearby residents.
The correlation between distance to airport entrance and housing price is significant negatively.
The premium of locating near the airport is approximately 2.73% for every 1 km nearer to the airport. This result is reasonable in Phnom Penh because property near by airport is good for restaurant and hotel after the departure of tourist.
5. Conclusion
This study examined the effects of proximity to public facilities on housing prices in Phnom Penh. We examined the effects by estimating various specifications of hedonic models, using sales price data of properties located in Phnom Penh city 2021-2022. The results showed that the public facilities have both positive and negative effects on local property values. Our results show that, the distance to CBD, park and airport has positive effect on housing price by 21.59%, 17.01% and 2.73% for every 1km closer to these facilities. For distance to market and train station shows negative effect and decrease the housing price by 11.82% and 23.95%, respectively. It is hoped that the data and results from this study can help urban planner to consider the necessity of public facilities as a priority in the urban planning process, as these facilities are part of living quality at one hand, at the other hand, they benefit to our society and property values.
There are some limitations in this study. First, the sample size of 150 may be small due to the time constraints for this research. Further, additional housing characteristics (e.g., property age, structural types, property types) should be considered. In this regard, future studies should include more sample size and property characteristics in order to confirm the accuracy of results in this study. Finally, future research is needed to observe more on environmental attribute like air quality and traffic noise that also has negative effect on housing prices because it would be valuable for the government to develop more sustainable city.
6. Acknowledgement
The authors of this paper thanks JICA LBE project for financial support during the data collection process. The fist author is also acknowledged to colleagues at Transport Study Unit, Institute of Technology of Cambodia, for their kind contributions into this study. The authors are responsible for any errors.
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