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International studies of housing attribute valuations have been conducted for numerous metropolitan markets. Examples include Africa, Europe, Asia, and North America. Similar research for Latin America and Mexico is less common (Figueroa 1993). To partially fill that gap in the literature, this effort employs a sample of 175 new houses for Ciudad Juarez, an important metropolitan economy in northern Mexico. All houses in the sample were completed and sold between November 2006 and April 2007. For each house, a total of 14 characteristics, both structural and locational, are utilized to model housing prices in this urban market.
As a growing economy, Ciudad Juarez offers an interesting case to study. Throughout Mexico, mortgage banking services are expanding at a rapid rate (Skelton 2006). Some aspects of the expanding housing stock in this city are well understood, such as increased demand for public utility services (Fullerton et al. 2006). To date, however, there have not been any attempts to quantify the contributions of physical attributes to the underlying values for housing structures in this market. To carry out such an effort, hedonic price equations for new dwellings are estimated using a combination of qualitative and numeric explanatory variables. It is expected that 13 of the 14 different characteristics will increase housing values. Several different equation estimates are utilized to analyze the data.
Subsequent sections of the material are organized as follows. A brief review of the literature is presented in next section. A description of the data and methodology is then provided. Empirical estimation results are summarized in the subsequent section. Concluding remarks and suggestions for future research are offered in the final portion of the paper.
Literature Review
Previous work on housing markets has broadly analyzed the demand for structural amenities and attributes. Some of this research utilizes data from developing countries, but the demand for housing in Mexico has generally been analyzed using other approaches. Most of the hedonic attributes pricing articles are based on the methodology proposed by Rosen (1974). It has been applied to data from a fairly large number of different housing markets (Blomquist and Worley 1981; Arimah 1992; Cheshire and Sheppard 1995; Pasha and Butt 1996). These studies have generally established that physical traits and neighborhood amenities play important roles in determining residential real estate values.
Can (1992) examines spatial neighborhood characteristics impacts on housing prices. Vicinity or adjacency externalities are added as a third set of explanatory variables. Two sets of estimates are completed. The first includes only structural and neighborhood amenities. The second also includes adjacency externalities. Similar to Case and Mayer (1996), results indicate that the inclusion of spatial factors in equation specifications improves model reliability. Cheshire and Sheppard (1995) reach similar conclusions based on data for towns in the United Kingdom. Empirical evidence in that study indicates that unobserved location amenities are systematically incorporated into housing prices.
Just as neighborhood amenities can increase housing prices, location disamenities will reduce housing values. A variety of examples, both observable and unobservable, have been documented in the literature in recent years. Examples include crime, environmental degradation, tax and fee burdens, and traffic congestion (Chay and Greenstone 2005; Cho 1997; Lang and Jian 2004; Pope 2008). Evidence reported in those, and other, studies suggests that hedonic model specifications should allow for both positive and negative factors. This can mean including variables that may raise housing values simply because they minimize the impacts of negative factors on that residence. In the context of Latin American housing markets, one example would be the presence of neighborhood guard posts.
Relatively few housing market studies have been completed for Mexico (Jones et al. 1993). Among those that have been completed, data constraints are fairly severe. In spite of that, indirect evidence has occasionally been uncovered that documents the demand for structural attributes that match individual household demographics and prospective owner preferences (Gonzalez 1997; Noguchi and Hernandez-Velasco 2005). Fairly critical housing shortages have also been documented for fast growing regional markets in northern Mexico such as Ciudad Juarez (Pena 2005). Given the international importance of the economy of Mexico, plus the potential construction industry and financial intermediary business opportunities associated with the demand for housing throughout much of the country, better understanding of its real estate markets appear warranted.
This study uses a hedonic modeling approach to analyze housing prices in Ciudad Juarez, Mexico. For this purpose, both structural and neighborhood amenities are included as potential explanatory variables. The sample includes a variety of single-family units built in different neighborhoods. Because Ciudad Juarez is relatively large, access to roads and social infrastructure is expected to play a crucial role in housing selection. The real-estate market in Juarez offers an opportunity to examine these relationships within the context of a growing urban economy in a middle-income country. To date, very few studies of this nature have been utilized to help quantify the nature of residential real estate demand in Mexico or other Latin American economies. This analysis attempts to at least partially fill that gap in the literature.
Data and Methodology
The objective of the hedonic modeling approach is to account for how attributes typically associated with residential structures affect housing prices. The methodology is widely employed by numerous organizations, including the United States Census Bureau in its quarterly housing market analysis. The Census Bureau approach incorporates twelve distinct structural characteristics (Rappaport 2007). Of those variables, six are used in this effort. Another eight amenities not used by the Census Bureau are also included in this study. The variables employed are listed in Table 1.
Despite widespread interest in the determination of housing prices, hedonic models have yet to be estimated for many metropolitan markets throughout Mexico. Ciudad Juarez has a unique identity as a result of its history, geographic location, and demographic characteristics. Consequently, amenity values will potentially differ what has been measured for other regional economies (Arimah 1992; Cheshire and Sheppard 1995; Pasha and Butt 1996). Similar to other large cities, Ciudad Juarez is laid out in a polycentric manner and residents commute to the various commercial and industrial employment centers. Residential zones have also developed around each of the employment centers in the city (Fuentes Flores 2001).
The value or price of housing (HP), measured in Mexican pesos, is modeled as a function of the fourteen explanatory variables shown in Table 1. Those right-hand side variables include the size of the property measured in square meters (LOT), the size of the house floor area measured in square meters (FLS), the number of bedrooms (BED), the number of bathrooms (BATH), and the number of stories (LEV). These are all numeric variables and, with the exception of LEV, should increase residential dwelling values. The data are from a residential real estate report that analyzes various aspects of the Ciudad Juarez market (Donjuan-Callejo 2007).
In addition, several qualitative variables are also included as structural amenity regressors. For parking spaces (PSP), (0) represents one space with no roof, (1) is one roofed space, (2) represents two spaces without roof, and (3) corresponds to two roofed spaces. For construction materials, (MATNC) is a binary dummy where (1) represents any material other than concrete and (0) is concrete walls. Floor materials (FLOORNC) include anything other than cement (1), while (0) represents the presence of cement as the floor material.
Location or neighborhood attributes are also measured using descriptive variables. They include binary indicators for the presence of the dwelling within a gated neighborhood (GATE), whether the neighborhood has a guard post (GUARD), and if the neighborhood has a park or green areas (PARK). Also, metropolitan area location (ML) is represented as location of the house close to a high speed road (2), a main avenue (1), or none of these (0). Proximity to an elementary school (SCH) and proximity to a commercial area (COMM) complete the explanatory variables list. As defined, all of the descriptive dummy variables are expected to increase housing values (Bible and Hsieh 2001; Lang and Nelson 2007).
The sample includes 175 observations for new houses completed and sold in Ciudad Juarez between November 2006 and April 2007. Because all of these units are less than 12 months old, neither age nor age squared are included as arguments in the hedonic specification (Dehring et al. 2007). Every house in the sample possesses a different bundle of characteristics. In this sense, each of the 175 observations differs from the rest. Summary statistics for the variables that comprise the sample are reported in Tables 2 and 3.
The geographic distribution of the housing units in the sample is fairly representative of new residential real estate activity in Ciudad Juarez. There are nine units from the northern sector of the city near the international Bridge of the Americas that crosses into El Paso, Texas. From the northeastern sector, where relatively ample supplies of flat land are available, there are 64 units. Similarly, from the eastern sector of the city, near the international Ysleta-Zaragoza Bridge, the sample contains data for 79 houses. From the southeastern sector, located east of the Ciudad Juarez regional airport where an ample supply of land is also available and public infrastructure is being built, there are 23 units.
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