EXTERNAL FACTORS, HOUSING VALUES AND RENTS: EVIDENCE FROM SURVEY DATA

This study uses a rich survey data to analyze housing values and rents in Istanbul, Turkey. In addition to variables related to location, household characteristics, and the physical characteristics of the property, the data involves a number of “qualitative” questions related to a set of external factors, such as the satisfaction level of the occupants with green area, parks, recreational facilities, transportation, distance to work, and noise. The paper identifies and compares the set of external factors that have a significant impact on property values and rents. Introduction When valuing residential property, one must consider the effects of the external factors as well as the characteristics of the property itself. Typically, these external factors are grouped together under a “location” variable. The principal reason for this practice is that the available data sets, such as the Multiple Listing Data sets, usually do not provide information on individual external factors. The other reason is that most of these external factors, such as noise, view, greenery, and accessability, are difficult to measure. The current study overcomes this problem by generating the data through an extensive survey. The survey was conducted in Istanbul, Turkey and includes questions about the occupants’ level of satisfaction / dissatisfaction with a number of external factors as well as questions about the physical, locational, and household characteristics of the property. The external factors included greenery, nearby parks, noise, recreational facilities, access to shopping centers, transportation facilities, distance to work, road conditions, distance to the street, and site conditions. The purpose of the project is to identify and compare the set of external factors have an impact on property values and rents. A number of earlier studies examined the role of individual external factors on housing prices. Hughes and Sirmans (1992, 1993) looked at the impact of different traffic levels within a neighborhood and compared the negative effects of excessive traffic (smoke, noise, danger, dust) with the positive effects of better access. Their study, based on 288 MLS residential sales within the Baton Rouge, Lousiana, metropolitian area, suggests a downward adjustment in the price of 11.49% for high traffic. The study by Li and Brown (1980) included data on air pollution, visual quality, noise and proximity to the ocean, rivers, recreational areas, schools, expressway interchanges, industry and commercial establishments. Using 781 MLS sales of single-family houses in 15 suburban towns located in the southeast sector of Boston in 1971, they concluded that while air pollution does not have a significant impact, visual quality has a positive impact and noise has a negative impact on property values. Proximity to the ocean, rivers, recreational areas, expressway interchanges, industrial and commercial establishments are highly valued while closeness to schools do not seem to matter. Correll, Lillydahl and Singell (1978) and a recent study by Lee and Linneman (1998) derived high amenity values for the greenbelts of Boulder, Colorado and Seoul, Korea, respectively. Similarly, Benson, Hansen, Schwartz and Smersh (1998) investigated the impact of a variety of views including ocean, lake and mountain, in Bellingham, Washington, and obtained a high willingness to pay for the view amenity. Another line of research attempted to determine whether proximity to power lines has an impact on the value of residential properties and found mixed results (Fisher and Lusht, 1995; Hamilton, Schwann and Carruthers, 1995; Colwell, 1990; and Furby, Gregory, Slovic and Fischhoff, 1988). A few studies established the negative effects of proximity to toxic waste sites on housing values (e.g., Kohlhase, 1991; Michaels and Smith, 1990). Dubin and Sung (1987) supplemented the 1978 Baltimore MLS data with geographic coordinates, census, and school data to estimate the rent gradient as a function of employment accessibility and neighborhood characteristics (crime, education, income, racial composition, and school quality). They found that the CBD (Central Business District) fails to exert a dominant influence on the rent gradient. They argued that the reason for the lack of significant relationship between housing prices and distance from the CBD in many empirical studies is because households value access to places other than the CBD, and that this is particularly true for cities with a