Evaluating and analysis of socio-economic variables on land and housing prices in Mashhad, Iran

Accepted Manuscript Title: Evaluating and analysis of socio-economic variables on land and housing prices in Mashhad, Iran Authors: Jafar Mirkatouli, Ali Hosseini, Reza Samadi PII: DOI: Reference:

S2210-6707(17)31196-4 https://doi.org/10.1016/j.scs.2018.06.022 SCS 1156

To appear in: Received date: Revised date: Accepted date:

6-9-2017 14-6-2018 16-6-2018

Please cite this article as: Mirkatouli J, Hosseini A, Samadi R, Evaluating and analysis of socio-economic variables on land and housing prices in Mashhad, Iran, Sustainable Cities and Society (2018), https://doi.org/10.1016/j.scs.2018.06.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Evaluating and analysis of socio-economic variables on land and housing prices in Mashhad, Iran

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Jafar Mirkatouli1, Ali Hosseini2,*Reza Samadi3

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Department of Geography and Urban Planning, University of Gorgan, Gorgan, Iran

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Department of Geography and Urban Planning, University of Tehran, 1417854151, Tehran, Iran

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author E-mail: [email protected]

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*Corresponding

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Providing empirical evidence of the links between socio-economic variables and land and housing prices in Mashhad. The paper outlines some theoretical works regarding the relationship between land prices and housing prices and also regarding how diverse variables (income, population, etc.) affect land and housing prices. Variables such as income and occupation have the direct positive relationship with land and housing prices. Social indicators such as family size and history of residence have lower effect on land and housing prices in Mashhad

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Highlights

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Department of Geography and Urban Planning, Ferdowsi University of Mashhad, Mashhad, Iran

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Abstract

This research aimed to investigate the role of socio-economic variables on the prices of land and housing in the city of Mashhad. From the results of the measured socio-economic variables of citizens, the highest coefficient was obtained by districts 1 and 9, while districts 5 and 3 had the

lowest value. The existence of a positive and direct relationship between socio-economic variables of the residents and land and housing prices, and the significance of the test (at the level of 95%) were approved. The results of the impact of socio-economic variables on land and housing prices,

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using path analysis, showed that the variables of income, occupational status, and educational level had the highest effect on the price of land and housing, while variables of family size and history of residence had the lowest impact on the price. Furthermore, the coefficient of determination (0.780) obtained from path analysis diagram showed that 78% of the total changes of the dependent variable (the price of land and housing) were explained by the analytical model.

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Keywords: Socio-economic variables, Land and housing prices, Social equity, Mashhad, Iran.

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1. Introduction

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Growth and development of cities are considered as a global issue (Pouriyeh et al, 2016), so that

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over 65% of the world population is predicted to live in cities by 2025 (Glasmeier & Nebiolo,

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2016). Meanwhile, the highest urban population growth is anticipated for Asia, Africa (Seto et al, 2012; Li et al., 2013; Fuseini & Kemp, 2016) and Latin America (DeFries et al., 2010; García-

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Ayllón, 2016). Cities are areas with an enormous quantity of available resources. Moreover, the economic growth is stimulated, and employment and various services are provided (Hosseini,

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Pourahmad & Pajoohan, 2015). On the other hand, rapid urban growth, which resulted from rapid expansion of city cores and the emergence of urban sprawl during 1970s and 1980s (Chitrakar, Baker & Guaralda, 2016), has caused a lot of problems and challenges in cities (see Abiodun, 1997; Wong & Tang, 2005; Cohen, 2006; Chen, Jia & Lau, 2008; Bhattarai & Conway, 2010; Jaeger et

al, 2010; Mirkatouli, Hosseini, & Neshat, 2015; Du, 2016). These problems seriously threaten the human environment, as well as economic and social sustainable development (He et al., 2018). One of the most important problems in cities is scarcity of urban land (Zou et al, 2014; Zhong,

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Chen & Huang, 2016), which has made land to become one of the most important requirements of city planners (Cohen & Galinienė, 2014).

Access to suitable, adequate and cheap pieces of land is a common concern in all countries, especially developing countries (Silvam, 2002; Lipton, 2009). In fact, land was, is, and will be the foundation of all human activities in the past, present and future, respectively (Muhallab Taha,

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2001). Its limited supply versus the increasing demand of urban population and increased rural-

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urban migration can lead to rapid increase in land and housing prices (Katz & Rosen, 1987; Tse,

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1998; Shen, 2007; Zhang, Cheng & Ng, 2013). Thus, there have been continuous increase in the

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values of land and housing in most countries, in the past 40 years (Chen et al, 2009; Zhang & Tang,

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2016; Ando, Dahlberg & Engström, 2017; Chen, Y. H., & Fik, 2017). The price of land, as a

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determining factor for the price of housing (Liu et al, 2013; Ossokina & Verweij, 2015; Jang & Kang, 2015; Shen, Y., & Karimi, 2017), is also used to determine land use type and change in cities

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(Davis et al, 2008). Since various factors determine the price of land and housing, these prices are

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known to vary with time and city locations, with prices varying between districts (Nichols et al, 2013), based on local and socio-economic levels. In other words, socio-economic characteristics

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of residents indicate socio-economic status among people (Marmot, 2005; Zahirovich-Herbert & Gibler, 2014; Campos & Guilhoto, 2017; Chia, Li & Tang, 2017), and have been used from time immemorial as a variable for studying inequity in population and rate of access to development resources (Miech & Hauser, 2001). Therefore, change in this status of residents and their physical

environment brings about changes in urban land and housing prices and accordingly changes in the internal structure of the city (Cervero & Duncan, 2006; Ozus & et al, 2007). The increase in the prices of urban land and housing in recent years has become one of the main

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concerns of people and authorities; therefore policy makers aim to regulate this price increase by identifying the factors affecting land and housing price, of which socio-economic status of residents, proximity to downtown and central business district (CBD), access to communication roads, proximity to urban services including green space and park, sports field, fire departments and health centers are noted as the most important factors.

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Based on the researches conducted on land and housing prices and with respect to the interviews

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conducted with experts and real estate agents in the city of Mashhad, the present research aimed to

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study the role of socio-economic variables on land and housing prices. It was believed that the

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socio-economic status of people living in the city affected the prices of land and housing..

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Meanwhile, use of land for tourism, which has a significant role in the economy of Mashhad, and

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other land uses, including business in the central area of the city, did not increase the prices of land and housing in such areas relative to the areas with higher socio-economic levels. Since many

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factors determine the price of land and housing, it is beyond the possibility of the present paper to

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discuss all of them.

Iranian cities, for example Mashhad, are faced with issues like population density, high price of

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land and housing, and high rents in some urban areas, which not only affects the low- and mediumincome earners, but also affects new constructions in and around the city. Land market has influenced the spatial development of these cities. Recognizing the relationship between land and housing price and socio-economic variables is very important for objective policy-making and

improving the development of the real estate market. This study provides empirical evidence of this relationship and assesses the most important factors influencing land and housing prices in Mashhad.

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The rest of the paper is organized as follows. Section 2 gives a brief review of previous relevant housing and land prices and socio-economic variables while section 3 outlines the study area. Section 4 discusses the data sources, definition of variables, and model specification. The empirical study and main results are presented in Section 5. Finally, the research results are discussed and

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concluded in section 6.

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2. Literature review

All economic activities including agriculture, construction and business require land (Nichols et al,

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2013). Land is an important element in the development and extension of cities (Li et al, 2016).

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Urban land is used for different purposes including housing, commercial, industrial, transportation and services (El Araby, 2003). One of the most basic roles of land is the provision of housing space

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for city residents (Vander Molen, 2002). Land and housing are the economic foundation of a

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country, the reason why government intervenes in land and housing market by policy making (Selim, 2009), which is based on the factors of supply and demand. Considering the mutual

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relationship between land and housing section and other economic sections, this policy is conveniently applied to this section by using money and land tools (Tsatsaronis & Zhu, 2004). Access to housing property is rather too expensive. Families spend, on average, 25% of their income on housing (Campos & Guilhoto, 2017). Thus, a large proportion of the household budget

is taken by this commodity, and a large percentage of the urban population is faced with some restriction in accessing good living environments (Campos & Guilhoto, 2017). Determining and estimating the price of land and housing is of high importance for planners and policy-makers

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(Malpezzi, 2003). If this type of estimate can identify the share of factors affecting the prices of these commodities, then it can be used in decision- and policy-making (Wen et al, 2005). Due to the complex and strong relationship between urban land and housing prices and the factors

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affecting them, we studied the perspectives in this regard.

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2.1 The cost-driven perspective

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Based on this perspective, housing price includes costs related to land purchase, development cost,

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marketing cost and development benefit (Yang, 2003; Bao, 2004). In fact, the increase in housing

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price is due to the shortage of land supply, and this price increase is directly proportional to increase

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in land price (Glaeser et al, 2005; Hui, 2004).

2.2 The derived demand perspective

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Based on this perspective, demand for land results from housing demand, and land price is

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determined by housing price (Li, 2005; Zhu & Dong, 2005). Therefore, the demand and price of land increases with increase in demand and price of housing (Feng & Liu 2006). Real and

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speculative demand for housing determines its price through the market mechanism. Increase of demand in the market of real assets causes more increase in demand for housing than the supply provided by market, consequently leading to increase in housing price, and finally increase in price of land (Liu, & Jiang, 2005).

2.3 The mutual causation perspective

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Housing price is not the only factor determining land price and vice versa, but there is a mutual relationship between both prices (land and housing), which is affected by other factors. In fact, both prices are affected by different market factors, which have various variables (Huang, 2005; Qu, 2005; Altuzarra & Esteban, 2011; Wen, Bu, & Qin, 2014; Zang, Lv & Warren, 2015; Zhang et al., 2016; Wu et al., 2017; Wen, Xiao & Zhang, 2017a and 2017b).

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As mentioned in the perspectives above, there are many influencing factors of the price of land and

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housing; some of the researches conducted in this regard will be noted in the following.

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Wen & Goodman (2013) assessed the effect of economic factors on land and housing price in the

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form of simultaneous equations model by using two-stage least squares method in time ranges of

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2000 to 2005. In this research, the effects of five internal and seven external variables on the price

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of land and housing, respectively were evaluated. The results showed that an internal relationship existed between land and housing prices, and income per capita was the most important factor

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affecting the price of land and housing. Using the data collected from single-family homes in Switzerland based on vector error correction

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model from 1978 to 2008, Bourassa et al., (2011) concluded that changes in the real price of housing were affected by changes in land price, real per capita gross domestic product and

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population growth of ages 30 to 40, and variations of land price were a function of changes in per capita gross domestic product and real cost of construction. Moreover, Li (2009) studied the changes in land and housing prices in Beijing from 1993 to 2005 and reported that based on different types of regression, the dependent variable included the average annual prices of land,

while the independent variables included population, gross domestic product, fixed asset investment, occupational, average salary, investment in housing and local revenue. In Mashhad city, the price of land is affected by the salary of the clerk. In other words, high salary earners tend

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to look for expensive lands to purchase. The results also indicated that increase in land prices was due to the increased demand for housing. Manning (1988) investigated the factors influencing the price of urban land by using data of land prices from 94 metropolises in the United States. His result showed that cost of construction, population growth, population density, climatic conditions and household income significantly affected the price of urban land. Winker & Jud (2002)

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investigated the factors affecting housing prices by using integrated data obtained from 130 big

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cities of the United States, and concluded that factors such as population growth, changes of

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income, construction costs and interest rates (with negative impact) highly impacted housing

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prices, respectively. Ozus et al., (2007) evaluated the effective factors on land and housing prices

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in Istanbul and reported that the difference between these prices in various districts were affected

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by location and socio-economic factors, and showed that planned areas had higher prices compared to other areas. Hu et al., (2016) investigated the relationship between land prices and the

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influencing factors in Wuhan city in China, and discovered a positive relationship between land

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price and its area. Besides, wealthy residents with higher socio-economic levels were willing to pay more for lands near the lake unless the underlying barriers prevent the rich from residing in

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these areas. Luttik (2000) noted that the premium on house price may be used as the guiding principle for optimizing the socio-economic value of ecological factors. Jaeger (2013) studied the determinants of urban land market outcomes in 46 California cities using GIS, and reported that factors such as family income and size, as well as natural barriers and constraints, affect the price of land. Taltavull de La Paz, López and Juárez (2017) evaluated the effect of different house prices

in an economic-mixed region where the costal amenities strongly attracts second home and temporal residents while the main region’s city is an administrative center in Alicante province, Spain. Their results showed that there was ripple effect between Orihuela city and the coastal and

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inland zones. Moreover, studying the dynamic interaction between regional housing prices in the United States, WU et al., (2017) showed that different trends in regional housing prices resulted in changes in time-varying correlation. They also found economic factor to be the most important parameter affecting the correlation of housing price between regions. Keskin (2008) investigated the factors influencing housing prices in Istanbul. Her results showed that factors such as living

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area size, living on the ground floor of a floor building, being in a secured site (with swimming

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pool and garage), and age of the building determined housing prices. Besides these determinants,

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the length of time lived in Istanbul, the average income of the household, neighbor satisfaction and

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earthquake risk of the area affected housing prices in Istanbul.

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As the above-mentioned researches showed, the pace of changes in land and housing prices was

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correlated with their zonal locations. In fact, the range of these prices in a district is due to features distinguishing one district from the other. Features such as social, economic and cultural

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classification of the residents, environmental status of the neighborhood and the area, proximity to

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city center, distance from shopping centers, proximity to the main roads and physical features of the building are the most important features determining land and housing prices in cities (Ozus,

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2007). In Table 1, some factors affecting the price of land and housing are shown, but only one of these factors was investigated in this study: the socio-economic variables of citizens and its role in the price of land and housing.

Table 1. Factors affecting the price of land and housing

A rich collection of measures can be created for the improvement of planning at the level of community by studying socio-economic variables of citizens in different researches. Long-term

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use of these variables in urban plans can have a positive impact on planning approach to achieve broader goals of the management (Bowen & Riley, 2003). As mentioned above, economic variables include economically active population, percentage of active employed population, total household income, and percentage of owners of residential units, and social variables include number of household members, level of education, life quality of citizens, degree of satisfaction with

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neighbors, and history of residence, which indicate life conditions of residents in a region.

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3. Study area

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Mashhad with a land mass area of 30000 acre in 2015 is located at 36º 20 ̍ N latitude and 59º 35 ̍ E

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longitude. The elevation of this city is 970 meter and its atmospheric distance from Tehran is 750 Km. The city of Mashhad is situated northeast of the Iran. Encompassing 13 districts, the city is

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home to the holy shrine of Imam Reza, which is one of the most important Shiite shrines, attracting

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more than 20 million pilgrims from all around Iran and other Muslim countries each year. This city has a population of 2772287, making it Iran's second largest metropolis according to the

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Statistical Centre of Iran (2011). In several past decades, there has been discontinuous sharp increase in urban land prices, which in turn affected housing price. This increase of land price adversely affected a large part of the society, especially low- to medium-income earners in areas of providing housing.

To analyze the price of land and housing, data and information over a long period should be assessed. Fig. 1 presents the price of each square meter of land and housing units in Mashhad during 1993 to 2015. In 1993, the price of land and housing was about $5/sq.m, and much

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fluctuation was not observed in the units’ prices until 2002. In 2002, the units experienced a price increase, so that the highest price increase was related to 2007 with two-fold increase; this caused significant increase in the price of land. From 2007 to 2011, there was a sharp reduction in the price of housing unit, especially land, but this trend was reversed for housing unit alone 2011 to 2013. Land and housing market in Mashhad has gone into recession from 2013 up to now, and large

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increase or decrease was not seen in the prices of both commodities.

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Fig. 1. The average price per square meter land and housing units in the city of Mashhad during

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the years 1993 – 2015 (Statistical Centre of Iran, 2011)

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4. Data, variables and methodology 4.1. Data

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To start the survey, 384 questionnaires were randomly distributed based on Cochran formula (Eq.

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1) among the statistical population. Two types of questionnaire were used for data collection; one for obtaining information related to socio-economic variables of citizens, and the other for

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collecting information about the prices of land and housing which was completed by referring to real estates. Furthermore, additional information about the price of land and housing in the studied area was collected from websites and statistics, including the level of income, employed or unemployed, the

owner of a residential unit or tenant, number and age of the members of the household, level of education, history of residence in the current location, satisfaction with living in the current place and other information in the questionnaire about socio-economic status of household's head. The

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information, either quantitative or qualitative, was obtained by referring to different places and interviewing the heads of household. Qualitative information was gathered based on Likert and five scale. The validity and reliability of the questionnaire was assessed using content validity and Cronbach's Alpha coefficient. The rate of alpha was obtained as 0.746 which shows the reliability

t 2 pq d2 n= 1 t 2 pq 1 + ( 2 − 1) N d

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of the questionnaire.

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(1)

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where: n – sample size; N – statistical population of city; p – percentage of people who have the

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attribute; q – the percentage of people who do not have that attribute; t2 – constant coefficients; d2 –

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the value of error (appropriate probable accuracy).

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In this study p = 50%, q = 50%, t2 = 1.96 (95% level) and d 2= 0.05%.

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1.962 ∗ 0.5 ∗ 0.5 0.052 𝑛= = 384 1 1.962 ∗ 0.5 ∗ 0.5 1+ ( − 1) 2 76804 0.05

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4.2. Variables

The most important variables used in this research include (Brown, 2000; Bendyk et al, 2017; Brasington & Hite, 2005 and Statistical Centre of Iran, 2011): - Active population: The percentage of employed and unemployed people who are at working age

- Occupational status: The percentage of people who participate in an economic activity in their region for a short period of even a week or a day.

investments belonging to the entire members of the household.

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- Total income of households: Fund and dollar value of goods and services for the work or

- Percentage of owners of residential units: The percentage of people in the region who own residential units and do not have rentals.

- Quality of life represents the life of the people residing in the region. Life quality is obtained from variables such as environmental, social, health, economic, spiritual and psychological.

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- Satisfaction with the place of residence: The percentage of personal satisfaction with their

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neighbors.

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- The history of residence: The average number of years spent living in the same region.

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- Educational level: The percentage of people with higher education living in the region.

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- Family size: Family size is obtained from the division of population by household number living

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in the region.

4.3. Methodology

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After collecting information, the socio-economic status was assessed using Morris model. To do so, qualitative information collected from the questionnaire data was converted into quantitative

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information. This model is one of the effective methods in the field of rational integration of developmental assessment variables. One of the important properties of Morris model is accurate rating and determination of the position of each variable at the level of regions. Then, data analysis was performed using SPSS, and to test the hypotheses, Pearson correlation was used. In addition,

considering that the impact of each of the socio-economic variables on land and housing prices was not the same and the variables also indirectly affected both prices, Path analysis was used as the best technique for evaluating both indirect and direct effects of the independent and dependent

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variables on each other (Kassani et al., 2014). Table 2 presents the relationships between the used variables.

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Table 2. Analytical relation between measurable variables in the studied confine

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5. Results

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In this section, first, the price of land and housing units in Mashhad was studied, and then the socio-

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economic variables were evaluated. Finally, the effect of these variables on the prices was studied

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by using statistical tests.

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5.1 The price of land and housing units in the city of Mashhad

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The average price of land and housing in different districts of Mashhad varied highly and had high fluctuation ranges. An interpolation of over 1000 points through Kriging method in ArcGIS reveals

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that higher average priced houses are located or confined in 1-Mashhad district with an average price of $800/sq.m than in 5-Mashhad district (with an average price of $200/sq.m) and other

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districts. Furthermore, the highest price of housing units in 2016 was seen in district 1 with the average price of $1000 /sq.m, while the lowest price with an average of about $250/sq.m was observed in district 5. In Table 3 and Fig. 2, the average prices of land and housing units per square meter in different districts of Mashhad are presented. Considering the information obtained about

the price of land and housing in this study, it appears that land and housing market in the city of Mashhad pulled a little out of recession in 2016 and dealing in this city will be boosted in the

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coming years, thus we will be facing an increase in the price of land and housing units in the city.

Table 3. The average prices of land and housing units per square meter in 13 districts of Mashhad in 2016

Fig. 2. The average prices of land and housing units per square meter in 13 districts of Mashhad in

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2016

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5.2 Evaluation of the socio-economic variables of citizens in Mashhad by using Morris model

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After data collection, qualitative data were converted into quantitative data by using bipolar scale

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method and then the socio-economic variables were evaluated by using Morris model, as shown in Table 4 and Fig. 3. The results showed that citizens of the district 1, with the coefficient of 0.92

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had better status and citizens of the district 5 with the coefficient of 0.16 had the worst compared

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to those of other districts in terms of the studied variables.

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Table 4. Socio-economic variables of citizens in 13 districts of Mashhad

Fig. 3. Distribution of socio-economic status of citizens in Mashhad using Morris model

5.3 Evaluation of the correlation between urban land and housing prices and socio-economic variables of citizens by using Morris model and Pearson correlation

To determine the relationship between urban land and housing prices and the economic variables

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of citizens, Pearson correlation coefficient was used. The coefficient (0.886) obtained from the relationship indicated a positive relationship between these two variables at the level of 99%.

Pearson correlation coefficient was also used to study the relationship between urban land and housing prices and social variables of citizens. The coefficient of 0.817 indicated a positive relationship between these two variables, which was significant at a probability level of 95%.

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The results obtained from Pearson correlation between socio-economic variables of citizens and

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the price of land and housing showed that higher level of these variables caused an increase in the

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prices of land and housing. For instance, as can be seen in Table 5 and Fig. 4 and 5, citizens living

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in districts 1, 9 and 11 had higher socio-economic level and thus the price of land and housing units

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in these districts was higher than the other districts. By contrast, in districts 5, 3 and 4 where socio-

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economic variables were low compared to other districts, land and housing prices were lower than other districts, although these districts had better situations in comparison with districts 1, 9 and 11

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in terms of other variables affecting these prices such as proximity to city center and main roads.

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Besides, these districts were provided with better urban services by the municipality of Mashhad. In fact, the only factor that caused a decrease in prices in these districts (5, 3 and 4) was the lower

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socio-economic level of the citizens.

Table 5. The correlation between socio-economic variables of citizens and the prices of urban land and housing by using Pearson correlation

Fig. 4. The correlation between economic status of citizens and the prices of urban land and housing in the city of Mashhad

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Fig. 5. The correlation between social status of citizens and the prices of urban land and housing in the city of Mashhad

5.4 The effect of socio-economic variables of citizens on urban land and housing prices using path

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analysis

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Path analysis was used to evaluate the impact of the studied variables on citizens, as the

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independent variables, and on urban land and housing price, as the dependent variable. Path

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analysis was first introduced by Sewall Wright in 1934 (Xu et al, 2016) in relation to the analysis of total correlation between two variables in a casual system. To do so, betas (β) coefficients of

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each variable were determined by using regression model based on enter method in path analysis

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model, and the direct and indirect effects of independent variable (socio-economic variables of citizens) on dependent variable (housing and land prices) were studied by path analysis diagram.

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The matrix related to the error coefficients of variables of causal model in the current research is

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presented in Table 6. As shown by the data, the amount of determination coefficient of casual model was 0.780, indicating that the analytical model can explain 78% of the changes in dependent

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variable. In Table 6, β coefficients of each of the independent variables on dependent variables and their significance levels are presented.

Table 6. Betas coefficients of independent variables on the dependent variable

After calculating the betas coefficients of independent variables, the direct and indirect effects of each variable on the dependent variable were calculated. The calculated betas coefficients, which

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were standardized, were considered as direct effects, and for calculating indirect effects, the betas coefficients of each path (for each independent variable) were multiplied by each other to obtain the dependent variable, and total effect of variables were obtained from total direct and indirect effects. In Tables 7 and 8 presents the calculations of direct and indirect effects of variables.

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Table 7. Direct and indirect effects of variables

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Table 8. Total direct and indirect effects of independent variables on the dependent variable

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The results related to the effects of independent variables on prices in the study area indicates that

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most independent variables, except for the following three variables active population, household size and history of residence, had direct and indirect effects on the dependent variable. The three

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outstanding variables indirectly affected the prices in the city. As shown in Table 7 and Fig. 6,

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income variable with the value of 0.895 had the greatest effect on these prices in the study area, followed by occupational status and education level. Given that two out of the three variables were

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economic variables in this study, it can be concluded that economic variables had more important role in increasing the prices in the city of Mashhad compared to the social variables. For instance, since doctors' offices, universities and commercial centers are located in districts 1, 9 and 11, doctors, universities teachers and businessmen who earn more caused an increase in the price of properties in these districts. On the contrary, due to the location of districts 5, 3 and 4 in suburbs

and slums of Mashhad, these districts were low in terms of income, job and education variables, and accordingly the prices were low. The interesting point in the results related to the calculation of direct and indirect effects is that

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household aspect with the coefficient of 0.264 had negative and reverse effect on prices, and its effect was only by life quality. In fact, the higher the number of family members (household size), the higher the cost of the family. Big sized family not only causes a direct impact on family life quality, but it also reduces the possibility of saving for the purchase of land and housing at affordable price.

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After obtaining beta coefficients for each variable, path analysis diagram can be drawn based on

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the obtained coefficients.

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Fig. 6. Diagram of path analysis along with beta coefficient

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In socio-economic research, identification of all the factors affecting dependent variable is not possible for the researcher. Therefore, variables of path analysis can always explain part of the

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variance of dependent variable; for that reason, what remains unknown as effect or factor in path

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analysis is shown by 𝑒 which is known as error quantity. The amount of 𝑒 shows the variance of the variable that could not be explained by the independent variables. Unexplained variance is

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obtained by the square root of 𝑒 value. If the value of 𝑒 2 is deducted from 1, the amount of explained variance, shown as 𝑅 2 , is obtained. To determine by how much the model presented in the path analysis model can explain the variance of dependent variable, determination coefficient of 𝑅 2 was used. 𝑅 2 values for all the variables are calculated and presented in Table 6. As shown

in the table, the value of determination coefficient was equal to 0.780, implying that 78% of the total changes of dependent variable (the price of land and housing) were explained by the above analytical model. Error coefficient or the value of e can be calculated by using the value of 𝑅 2 , as

𝑅2 = 1 − 𝑒 2

→ 0.780 = 1 − 𝑒 2

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follows:

→ 1 − 0.780 = 𝑒 2 = 0.22

Therefore, it can be said that the casual model presented in this research cannot explain 22% of the

U

variance of dependent variable, suggesting the efficiency of the model.

N

6. Discussion and conclusions

A

In urban studies, land and housing have always been introduced as a commodity with dual

M

properties. Besides having consuming value, housing is also an asset with economic investment

D

capability in the market. Therefore, the price of land and housing and the factors affecting their

TE

price are important for homeowners, investors, tax auditor and other participants in the market, as well as municipal authorities for proper planning in the city.

EP

Based on the data collected by field observation, statistics and referring to statistical center, more than 1000 points were entered in ArcGIS, then a map of the land and housing prices distribution in

CC

the study area was drawn, from which the average prices of land and housing unit in Mashhad were determined as $411/sq.m and $524/sq.m, respectively in 2016. Study of socio-economic variables

A

of citizens using Morris also showed that districts 1 and 5 had the highest and lowest rates in terms of socio-economic variables, respectively. The results obtained from Pearson correlation showed that there was direct positive relationship between socio-economic variables and the price of land and housing in the city of Mashhad. Furthermore, the results obtained from path analysis revealed

that income and occupational status variables had the highest effects on the prices in the city of Mashhad, respectively, while social variables such as family size and history of residence had the lowest effect on the prices. In fact, different levels of socio-economic stratification of citizens that

SC RI PT

existed in Mashhad caused a huge difference in the price of land and housing among the districts of the city. Citizens with high levels of income and social welfare seek the most favorable conditions; for example, due to the proximity near the widest orchard (Malek Abad) and park (Melat) of the city, districts 1 and 9 have good weather, minimum polluting sources, and the highest attainable standard of physical and mental health so that people can create an ideal environment

U

for themselves and their family. When these conditions and other favorable conditions are

N

provided, classes with high socio-economic variables reside in these areas, which in turn causes

A

rapid increase in land and housing prices over time compared to other areas of the city, and people

M

with low socio-economic levels thus cannot afford land and housing in these areas; these variables

D

show an apparent social injustice in the city so that people with high socio-economic levels can use

TE

ideal services of the city while people living on the outskirt of the city do not have access to these favorable conditions. In fact, more favorable socio-economic status (regions 11, 1 and 9) led to an

EP

increase in the price of land and housing in the city of Mashhad. Following this increase and given

CC

that municipal revenue (tolls and municipal taxes) in these regions is high, for satisfaction of the prosperous people of these regions, more facilities and services are provided by urban authorities

A

to these areas and the result is inequality between different regions of the city. Thus, according to the mentioned conditions, conditions should be provided so all the members of the society enjoy similar conditions in the city without any discrimination and inequality in the distribution of social, economic, environmental, cultural and political resources.

The distribution of urban services among all residents of the city must be fair so that moving towards sustainable urban development can be possible. In fact, one the limitations of this research was the lack of paying attention to the issue of urban justice, which is the right of all the citizens

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of Mashhad. Other limitations include the lack of consideration of other factors affecting the price of housing land, which should be studied in future researches. It is recommended that citizens in districts with low socio-economic variables, for example districts 5, 3, and 4, should be provided with more municipal services. More municipal services in less developed districts can lead to the increase in the price of land and housing in these districts, and thus the variation in prices among

U

districts reduces and social equality at the city level is realized. High tax in districts with high land

N

and housing prices as well as proper monitoring by urban officials to prevent land and housing

A

speculation in these districts of the city are other recommendations of this paper. It is also suggested

M

that uncultivated land in the northern part of the city should be assigned to people living in the

A

CC

EP

TE

D

downtown part of the city with appropriate price with the provision of loans and other facilities.

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400 350

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300

Price

250 200 150 100

U

50

A

N

0

Land price

M

Housing price

D

* The price of housing unit or building is the amount of money exchanged between seller and buyer for the deal of building or dwelling unit in the form of certain deal. The price includes cost of land as well. ** The price of land is the amount of money that is paid to seller by buyer for the final deal of land. The ruined buildings are considered as land.

TE

Fig. 1. The average price per square meter land and housing units in the city of Mashhad during

A

CC

EP

the years 1993 – 2015 (Statistical Centre of Iran, 2011)

SC RI PT U

Fig. 2. The average prices of land and housing units per square meter in 13 districts of Mashhad in

CC

EP

TE

D

M

A

N

2016

A

Fig. 3. Distribution of socio-economic status of citizens in Mashhad using Morris model

1000

R² = 0.9191

900 800 700

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Land and housing prices (US$)

1100

600 500 400 300 200 0.4

0.5

0.6 0.7 0.8 Eeconomic status of citizens (%)

0.9

1

U

0.3

Fig. 4. The correlation between economic status of citizens and the prices of urban land and

N

housing in the city of Mashhad

A M

1000 900

700

TE

600 500 400 300 200

R² = 0.8954

D

800

EP

Land and housing prices (US$)

1100

CC

0.4

0.5

0.6 0.7 0.8 Social status of citizens (%)

0.9

1

A

Fig. 5. The correlation between social status of citizens and the prices of urban land and housing in the city of Mashhad

0.727 Income level (x3)

0.349

Active population (x1)

0.455

0.551

0.350

0.316

Land and housing prices

0.265

0.319

0.480

0.351

0.353 Educational level (x8)

- 0.550

0.552

U

Quality of life (x5)

N

History of residence (x7)

M

A

0.653

0.634

EP

TE

D

Fig. 6. Diagram of path analysis along with beta coefficient

CC

Family size (x9)

0.220

0.455

Home ownership (x4)

A

SC RI PT

Occupatio nal status (x2)

Satisfaction with the place of residence (x6)

Table 1. Factors affecting the price of land and housing Definition Distance from land and housing to the nearest Central Business District

D-road

Distance from land and housing to the nearest roads

D-station

Distance from land and housing to railway and bus station Residential, commercial, training and etc.

Type of land use

Existence of facilities and services urban near the land or housing such as parks, green places, sports spaces, libraries, hygienic and therapeutic center and etc.

D-school Building features

Distance from land and housing to the nearest school Features such as floor area, age, parking, elevator, central heating system, living room area, kitchen area and number of story Features such as active population, occupational status, income level, home ownership, investment of fixed assets and growth domestic product Features such as quality of life, satisfaction with the place of residence, history of residence, educational level, family size, population growth and population density

Brasington & Hite, 2005; Yue & Hongyu 2004; Ozus et al, 2007; Tan et al, 2017, Brown, 2000; Robson et al, 2000

A

N

He et al, 2010 Ozus et al, 2007; Brasington & Hite, 2005; Head & Mayer, 2004 Brasington & Hite, 2005; Wang & Jiang, 2016; Wen & Goodman, 2013; Li, 2009; Bendyk et al, 2017

M

Social status of citizens

U

Facility

Economic status of citizens

CC

EP

TE

D

*D= Distance

A

Source Colwell & Munneke, 1999; Wang, 2009;Partridge et al, 2009; He et al, 2010 Adair et al, 2000; He et al, 2010 He et al, 2010; Ozus et al, 2007 Geoghegan, et al, 1997; Ihlanfeldt, 2007; Liang et al, 2016 Tyrväinen & Väänänen 1998; Kong et al, 2007

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Variable D-CBD*

Table 2. Analytical relation between measurable variables in the studied confine

Dependent

Variant Land and housing price

Indicators - Low price - Average price - High price

Statistics

- Active population Economic status of - Occupational status - Income level citizens

Social status of citizens

- Home ownership - Quality of life - Rate of satisfaction with the place of residence - History of residence - Educational level - Family size

- The effect of economic and social status of citizens on urban land and housing prices using path analysis

- Assessment of the economic and social status of citizens by using Morris model

A

CC

EP

TE

D

M

A

N

U

Independent

-Correlation between urban land and housing prices and economic and social status of citizens by using Pearson correlation

SC RI PT

Variable type

Table 3. The average prices of land and housing units per square meter in 13 districts of Mashhad in 2016 Price of urban housing 1000 571 286 314 250 343 429 657 829 457 714 329 629 524

SC RI PT

Price of urban land 800 429 251 229 200 321 274 526 571 343 663 263 471 411

A

CC

EP

TE

D

M

A

N

U

Districts 1 2 3 4 5 6 7 8 9 10 11 12 13 (Samen) Average

Educational level

Family size

Social status of citizens

Eeconomic and social status of citizens ranking 5

28

0.42

3.50

0.26

0.18

12

29

0.43

3.60

0.24

0.28

11

23

0.41

3.90

0.19

0.16

13

35

0.53

3.60

0.43

0.39

9

29

0.49

3.40

0.47

0.47

8

20

0.62

3.20

0.65

0.65

6

38

0.64

3.30

0.85

0.83

2

32

0.63

3.40

0.62

0.53

7

28

0.65

3.50

0.78

0.80

3

10

0.45

3.50

0.32

0.39

10

0.57

35

0.55

3.30

0.74

0.68

4

0.51

28.31

0.54

3.44

0.54

0.53

0.20

0.09

7.95

0.09

0.19

0.23

0.24

0.91

0.64

40.00

0.67

3.90

0.86

0.92

0.31

0.32

10.00

0.41

3.10

0.19

0.16

0.81

750

0.89

1.00

0.91

0.87

0.79

562

0.77

0.66

0.69

0.57

3

0.83

0.63

320

0.58

0.09

0.31

0.39

4

0.83

0.73

391

0.67

0.33

0.41

0.32

5

0.80

0.71

365

0.55

0.13

0.42

0.42

6

0.83

0.71

459

0.67

0.34

0.45

0.41

7

0.85

0.76

490

0.68

0.47

0.52

0.48

8

0.87

0.74

601

0.79

0.64

0.74

0.50

9

0.87

0.80

650

0.85

0.80

0.85

0.59

10

0.84

0.72

490

0.69

0.42

0.51

0.52

11

0.87

0.81

670

0.82

0.81

0.88

0.64

12

0.83

0.75

510

0.72

0.46

0.49

0.55

13 (Samen)

0.85

0.77

694

0.70

0.62

0.81

Mean

0.85

0.75

534.77

0.72

0.52

0.61

Standard Deviations

0.03

0.05

128.41

0.10

0.26

Max

0.91

0.81

750.00

0.89

1.00

Min

0.80

0.63

320.00

A

CC

EP

TE

D

0.55

N

A

0.09

U

0.91

2

SC RI PT

History of residence

1

0.67

Eeconomic status of citizens Quality of life

0.92

0.67

1

M

0.86

3.40

Home ownership

3.10

0.51

Income level

0.67

40

Occupational status

21

Active population

0.62

districts

Satisfaction with the place of residence

Table 4. Socio-economic variables of citizens in 13 districts of Mashhad

Table 5. The correlation between socio-economic variables of citizens and the prices of urban land and housing by using Pearson correlation

Economic status of citizens Social status of citizens

C-C 0.886 0.817

Urban land and housing price with economic and social variables of citizens Sig 0.000** 0.005*

SC RI PT

Variables

Findings of the research, 2016 a: C

- C = Correlation Coefficient

= Significance

* Making sense of 95%

A

CC

EP

TE

D

M

A

N

U

** Making sense of 99%

b: Sig

Table 6. Betas coefficients of independent variables on the dependent variable β Beta Variables coefficient

β Beta Coefficient of coefficient determination (R2) Quality of life 0.480 Satisfaction with the 0.634 place of residence 0.780 History of residence Educational level 0.265 Family size -

Active population Occupational status

0.350

Income level Home ownership

0.727 0.319

A

CC

EP

TE

D

M

A

N

U

SC RI PT

Variables

Table 7. Direct and indirect effects of variables

𝑋1 → 𝑋2 → 𝑋10 𝑋1 → 𝑋3 → 𝑋5 → 𝑋10 𝑋1 → 𝑋3 → 𝑋10 𝑋1 → 𝑋2 → 𝑋5 → 𝑋10

Indirect

SC RI PT

Direct and indirect effects of the active population (X1) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 𝑋1 → 𝑋10 (0.551) × (0.350) = 0.193 (0.349) × (0.351) × (0.480) = 0.059 (0.349) × (0.727) = 0.289 (0.551) × (0.220) × (0.480) = 0.058

Total indirect effects

0.563

Total indirect effects

0.563

N

(0.455) × (0.351) × (0.480) = 0.076 (0.455) × (0.727) = 0.331 (0.220) × (0.480) = 0.106

A

𝑋2 → 𝑋3 → 𝑋5 → 𝑋10 𝑋2 → 𝑋3 → 𝑋10 𝑋2 → 𝑋5 → 𝑋10

Indirect

U

Direct and indirect effects of the occupational status (X2) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 0.350 𝑋2 → 𝑋10

0.513 0.863

M

Total indirect effects Total indirect effects

D

Direct and indirect effects of the income level (X3) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 0.727 𝑋3 → 𝑋10 𝑋3 → 𝑋5 → 𝑋10

TE

Indirect Total indirect effects

(0.351) × (0.480) = 0.168 0.168

CC

EP

Total indirect effects 0.895 Direct and indirect effects of the home ownership (X4) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 0.319 𝑋4 → 𝑋10 Indirect

𝑋4 → 𝑋5 → 𝑋10

A

Total indirect effects Total indirect effects

(0.552) × (0.480) = 0.265 0.265 0.584

Direct and indirect effects of the quality of life (X 5) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 0.480 𝑋5 → 𝑋10 Indirect

𝑋5 → 𝑋8 → 𝑋2 → 𝑋10 𝑋5 → 𝑋8 → 𝑋10

(0.455) × (0.353) × (0.353) = 0.056 (0.455) × (0.265) = 0.120

Total indirect effects Total indirect effects

0.176 0.656

Direct and indirect effects of the satisfaction with the place of residence (X6) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 0.634 𝑋6 → 𝑋10 − −

0.634

SC RI PT

−

Indirect Total indirect effects Total indirect effects

Direct and indirect effects of the history of residence (X7) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 𝑋7 → 𝑋10 (0.653) × (0.634) = 0.414 Indirect 𝑋7 → 𝑋6 → 𝑋10 0.414

Total indirect effects

0.414

U

Total indirect effects

Total indirect effects

M

→ 𝑋10 → 𝑋5 → 𝑋10 → 𝑋10 → 𝑋10

TE

Total indirect effects

→ 𝑋2 → 𝑋3 → 𝑋5 → 𝑋3

D

Indirect

𝑋8 𝑋8 → 𝑋2 𝑋8 𝑋8

A

N

Direct and indirect effects of the educational level (X 8) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 0.265 𝑋8 → 𝑋10 (0.353) × (0.350) = 0.123 (0.353)×(0.455)×(0.351)×(0.480) = 0.059 (0.316) × (0.727) = 0.229 (0.353) × (0.455) × (0.727) = 0.117 0.497 0.762

EP

Direct and indirect effects of the family size (X9) on the price of urban land and housing (X10) Type of effect Paths The effect of β coefficients Direct 𝑋9 → 𝑋10

A

CC

Indirect

𝑋9 → 𝑋5 → 𝑋10

(- 0.550) × (0.480) = - 0.264

Total indirect effects

- 0.264

Total indirect effects

- 0.264

Table 8. Total direct and indirect effects of independent variables on the dependent variable Direct effect

Indirect effect

Total effects

Ranking

X1 X2 X3 X4 X5 X6

0.350 0.727 0.319 0.480 0.634

0.563 0.513 0.168 0.265 0.177 -

0.563 0.863 0.895 0.584 0.657 0.634

7 2 1 6 4 5

0.265 -

0.414 0.497 - 0.264

0.414 0.762 - 0.264

8 3 9

A

CC

EP

TE

D

M

A

N

U

X7 X8 X9

Active population Occupational status Income level Home ownership Quality of life Satisfaction with the place of residence History of residence Educational level Family size

SC RI PT

Independent variable