Environment and Behavior

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Sources of Discontent : Residential Satisfaction of Tenants From an Internet Ratings Site Russell N. James, Andrew T. Carswell and Anne L. Sweaney Environment and Behavior 2009 41: 43 originally published online 6 May 2008 DOI: 10.1177/0013916507310031 The online version of this article can be found at: http://eab.sagepub.com/content/41/1/43

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Sources of Discontent Residential Satisfaction of Tenants From an Internet Ratings Site

Environment and Behavior Volume 41 Number 1 January 2009 43-59 © 2009 Sage Publications 10.1177/0013916507310031 http://eab.sagepub.com hosted at http://online.sagepub.com

Russell N. James III Andrew T. Carswell Anne L. Sweaney University of Georgia

This article presents the first systematic analysis of residential satisfaction ratings from the largest consumer comment Web site for U.S. apartment residents, www.ApartmentRatings.com. Using the 464,281 tenant satisfaction ratings posted from January 1, 2000, to January 1, 2007, we examine the relative importance of seven core factors in determining tenant satisfaction: parking, noise level, landscaping, safety, building construction, office staff, and maintenance service. Cross tabulation, ordered logit, probit, and path analysis models all point to tenant relations with management office staff as the most influential factor in tenant satisfaction. The fact that the manager–tenant relationship exists for tenants but not for homeowners may help to explain why the gap in residential satisfaction between owners and renters persists even when controlling for other physical environmental characteristics. Keywords:

residential satisfaction; tenants; property management

T

oday, one-third of Americans live in rented homes (Katz & Turner, 2007). As concerns over traffic, pollution, and environmental impact begin to limit the acceptability of unchecked urban sprawl, many are suggesting a renewed emphasis on higher density residential environments (Kahn, 2000; Obrinsky & Stein, 2007). In the United States, such dense, multifamily housing structures are almost exclusively rental housing. As Glaeser and Shapiro (2003) comment, “Homeownership is almost perfectly Authors’ Note: The results and views expressed are those of the authors and do not reflect the views of Apartment Ratings, Inc., or ApartmentRatings.com, a division of Internet Brands, Inc. Only publicly available data were made available to the researcher. Correspondence concerning this article should be addressed to Dr. Russell N. James III, University of Georgia, Athens, GA 30602; e-mail: [email protected]. 43 Downloaded from eab.sagepub.com by guest on February 19, 2013

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Environment and Behavior

linked with the type of housing structure” (p. 40). The extent to which rental housing is a large and growing part of our national residential environment suggests the importance of understanding what generates tenant satisfaction. Further, tenant dissatisfaction leads to increased mobility and transience (Morris & Winter, 1978; Speare, 1974). Such transience can diminish neighborhood and community social capital development (Putnam, 1995), and negatively affect educational outcomes for children (Adam & ChaseLansdale, 2002; Eckenrode, Rowe, Laird, & Brathwaite, 1995). The United States has a long history of distinguishing tenants as a separate class from property owners. The desire to become landowners and escape from oppressive European landlords often motivated the earliest European settlers (Kim, 1978; Kraus, 1971). This focus on property ownership showed itself in early suffrage laws. It was not until 1860 that tenants were given the right to vote in federal elections (Martin, 1976). In the 20th and 21st centuries, a substantial amount of public policy and political rhetoric has been devoted to the goal of reducing tenancy and increasing homeownership (Dreier, 1982). This cultural background supports a perception of the landowner (or landowner’s representative) as the dominant, superior individual in the landlord–tenant relationship. One might argue that, in modern times, tenants are simply customers. Indeed, it is quite possible for a particular property manager to see his or her role as a servant to the needs of the resident tenants. However, it is just as likely for a property manager to see himself as “lord of the manor,” viewing tenants, either paternalistically or dismissively, as vassals or serfs of a lower class (Dreier, 1982). This variability in management approaches could result in dramatically different tenant experiences based on the attitudes of property managers. Specifically, different property managers’ approaches could directly affect tenants’ relative standing and perceived control, vis-à-vis the property manager, even outside of the physical conditions of the housing unit. Thus, the potential for tenant dissatisfaction resulting from property manager attitudes may be wholly separate from any objective characteristics of the living environment. That human satisfaction is closely linked to relative standing and perceived control over one’s environment is a finding common to studies in many domains of life satisfaction. Rotter (1966) presents the general framework of a “locus of control” approach reflecting the degree to which a person expects to control his environment (internalized locus of control) or be controlled by his environment (externalized locus of control). Across many domains, an internalized locus of control is associated with greater satisfaction. For example, perceived control is a major driver in


James et al. / Sources of Discontent

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self-reported job satisfaction (Kulcarni, 1983; Spector, 1986). Similarly, self-reported life satisfaction is higher in societies where individuals can directly participate in making personal and political decisions, and lower where a ruling class has this decision-making authority (Frey & Stutzer, 2000; Welsch, 2003). Tenants may experience greater externalized locus of control than homeowners do, as tenants have more limitations on their ability to control their housing environment. Tenants typically cannot alter or modify the structural characteristics and are subject to being removed at lease’s end. A locus of control approach has also been successfully applied to measuring differences in residential satisfaction of public housing tenants (LeBrassuer, Blackford, & Whissell, 1988). The presence of a manager who emphasizes the tenant’s lower relative standing would also heighten the negative effects of this tenure status (Ahlbrandt & Brophy, 1976). For examples of how relative standing drives life satisfaction in financial matters, see Diener and Oishi (2000), Easterlin (1974, 1995), Luttmer (2005), and Myers (2000).

Online Consumer Comments Online consumer comment sites are an increasingly important way for current customers to express their opinions and for potential customers to learn from others’ experience. This article presents the first systematic analysis of residential satisfaction ratings made on the dominant U.S. Internet site posting evaluations of apartments. The data set from Apartment Ratings.com includes nearly one-half million ratings posted by users between January 1, 2000, and January 1, 2007. During that period the annual volume of postings on ApartmentRatings.com increased more than tenfold, reflecting the growing importance of this medium as a channel for communicating residential satisfaction and dissatisfaction. Consumer rating sites constitute an important, but nonstandard, source of information. Long before the Internet, consumers were found to consider “other consumers” as a more trustworthy source of information than experts (Kelley, 1967). Correspondingly, modern consumer comment sites are significant both for what they reveal about true consumer opinion, and because the comments directly influence other consumers’ choices (Chevalier & Mayzlin, 2006; Huang & Chen, 2006; Senecal & Nantel, 2004). Indeed, discussion forum comments have a stronger impact on consumer choices than traditional advertising (Bickart & Schindler, 2001; Chiou & Cheng, 2003). Because of this effect, consumer ratings can be influential and important even when they do not accurately reflect underlying consumer


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sentiment. In the case of tenant satisfaction ratings, prospective tenants may accept posted ratings about apartments as accurate—presumably coming from residents who have more information than the prospective tenant—or prospective tenants may simply use them to identify issues requiring further investigation before committing to a lease. Given the incentives for producers to post false positive ratings on consumer comment Web sites (Dellarocas, 2006; Morin, 2003), consumers often pay more attention to the content of negative ratings (Mayzlin, 2006). Recognizing this incentive, we employ some analyses using only negative ratings— those where the resident would not recommend the apartment to a friend— rather than examining all posted ratings as a group. In contrast to rating systems such as seller feedback ratings on eBay.com where 99% of all ratings are positive (Resnick & Zeckhauser, 2002), the majority of customer ratings on ApartmentRatings.com data are negative, providing a large set of observations where, presumably, false positive posting by managers is unlikely. The ratings on ApartmentRatings.com allow respondents to express their experienced level of residential satisfaction. The generally negative tenor of the ApartmentRatings.com reviews is broadly consistent with other studies of tenant satisfaction. The self-reported residential satisfaction of tenants falls below that of similarly situated homeowners (Rohe & Stegman, 1994; Rossi & Weber, 1996). This deficiency persists even when controlling for quality of the physical structure and sociodemographic characteristics of residents (Elsinga & Hoekstra, 2005). Although some have suggested that this satisfaction gap is driven by uniquely American concepts of homeownership and the “American dream” (Saunders, 1990), the same gap exists in European Union nations such as Spain, Italy, Ireland, United Kingdom, Netherlands, Denmark, and Greece (Elsinga & Hoekstra, 2005). While the satisfaction disparity appears well established, less is known about its causes. The detailed nature of the ApartmentRatings.com questionnaire allows for an inquiry into the importance of various satisfaction components in influencing residential satisfaction and dissatisfaction among tenants. We posit that the relationship of tenants and property managers is a driving factor in this tenant satisfaction gap. The lower relative standing of tenants creates a situation in which tenants have less control over their residential environment. The presence of a landlord—a relationship that does not exist for homeowners—simultaneously reduces the control and the relative standing of tenants. Homeowners rarely have a single individual who commands as much authority over their residential environment as do tenants. While most homeowners must meet mortgage payments, homeowners are typically free to refinance and change mortgagors should one creditor



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prove too bothersome. Tenants, however, cannot change problematic property managers without changing residences.

Data To consider the significance of the manager–resident relationship to tenant satisfaction we examine data from the largest consumer comment Web site for U.S. apartment residents, www.ApartmentRatings.com. This data set includes all 466,386 comments posted between January 1, 2000, and January 1, 2007. Respondents select their particular apartment complex and then typically provide information such as number of bedrooms, bathrooms, rent, amount of deposit, and the length of their residency at the apartment. Although registration information is required to post a rating, there is no verification methodology to insure that only current or former residents are posting. Respondents rate the apartment on seven different areas, provide an overall rating, and indicate whether they would recommend this apartment to a friend. Respondents rate each area from one star (the lowest) to five stars (the highest), corresponding to different evaluative statements. The evaluative statements and corresponding number of stars for the seven areas are as follows: Parking (1) Parking is a total nightmare, (2) One assigned spot, but a problem with 2 cars, (3) Parking is adequate, (4) Ample parking, (5) Parking is ample, well-lit, and secure. Noise level (Can you hear your neighbors all too well?) (1) Noise is a serious problem, (2) Often noisy, (3) Occasionally noisy, (4) Minimal noise, (5) Very quiet. Grounds (Overall appearance of the entire community; upkeep and landscaping) (1) Poor landscaping and upkeep, (2) Uninspired and dull, (3) Tasteful but nothing special, (4) Attractive and well-kept, (5) A thing of beauty. Safety (Outdoor lighting, layout enhances visibility, shrubs are cut back from walkways, etc.) (1) I did not feel safe, (2) Somewhat unsafe. Poorly lit, shrubs encroach on walkways, unsafe area, (3) Don’t know of any problems. Seems pretty safe to me, (4) Shrubs cut back from walkways, good lighting, safe area, (5) Very safe. Construction (quality of the fixtures, building material, sound and heat insulation, layout and design) (1) Falling apart. Shoddy materials and workmanship, (2) Cheap fixtures, materials, or poor insulation, (3) Nothing special, (4) Built well, (5) Top quality. Insulation, fixtures, and building materials appear high quality.


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Maintenance service quality (maintenance people fix problems correctly) (1) Very poor workmanship, (2) Poor workmanship. May require multiple trips by maintenance, (3) Usually fixed correctly, (4) Always fixed correctly, (5) Very good work. Always fixed right the first time. Office staff (Professionalism, knowledge, helpfulness, and overall service level) (1) Not helpful. Generally a pain to deal with, (2) Slow and shiftless, (3) Provide a basic level of service, (4) Helpful and nice. Care about helping residents and solving problems, (5) Staff really have its act together! Obviously committed to delivering great service to residents. Overall (Compared to other apartments, how does this community rate in general?) (1) Rates poorly on basically all levels, (2) Below average. Not the greatest place, but not the worst, (3) Average compared to others, (4) Superior to most others, (5) An excellent community in all regards.

Results Descriptive Statistics Table 1 provides a quick overview of the rating averages and standard deviations, along with the number of responses. Only 43% of respondents would recommend the apartment to a friend. Respondents indicate the year their lease in the apartment community has or will expire. Table 1 reflects the generally growing number of respondents in each consecutive lease-ending year. Given the varied definitions of each star rating category described above, the use of means and standard deviations must be undertaken with care and understanding. For example, the subjective distance between a one-star and a two-star rating may not be equivalent to the gap between a three-star and four-star rating. Similarly, a three-star rating in one category may represent a significantly lower (or higher) threshold than a three-star rating in another category, given that the star-rating definitions are different for each category. In order to avoid masking these distinctions, Table 2 presents the star rating frequencies for each category. Table 2 also presents these frequencies when limiting the observations to those who would not recommend the apartment to a friend. In both the data set as a whole and the nonrecommending subset, the “office staff” category was most likely to receive a one-star rating. In Table 2, a “below-average” rating is indicated when the number of stars given to that category was below the average number of stars given to all other categories by that particular reviewer. When a respondent rates one category lower than other categories, it may represent a differential focus on a particular item. Conversely, an unhappy resident who rated all categories as



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Table 1 Descriptive Statistics Variable

Mean

Overall Office staff Parking Noise Maintenance Safety Building Grounds Recommend Rent (2006 dollars) Bedrooms Bathrooms Lease ending year 2000 and prior 2001 2002 2003 2004 2005 2006

2.73 2.79 2.91 2.90 2.97 2.91 2.80 3.03 43.0% $913.78 1.38 1.31

Standard Deviation

Completed Responses

1.46 1.59 1.38 1.37 1.44 1.38 1.3 1.28

464,281 464,278 464,284 464,283 464,280 464,283 464,282 464,281 451,821 309,840 466,386 466,386

$432.34 0.92 0.5

2.8% 3.3% 8.6% 13.9% 22.9% 19.9% 28.7%

12,999 15,322 39,928 64,535 106,320 92,392 133,249

one star would not indicate a particular focus on any one category and hence would not appear in the “below-average” column for any category. The bottom half of Table 2 reports the ratings of unhappy residents—those who would not recommend the apartment to a friend. Among these nonrecommenders, the office staff category was by far the most likely to receive a “below-average” rating. Similarly, among the same nonrecommending residents, the office staff category was far less likely to receive a rating higher than the respondent’s average category rating. Nonrecommending respondents appear to direct their negativity toward office staff more frequently than toward any other category.

Probit and Ordered Logit Analyses Table 3 presents an ordered logistic regression using the overall rating as the dependent variable on two data sets, one including all observations, and the other including only those observations where the resident would not recommend the apartment. We employ the ordered logistic model because the star-rating numbers are associated with a qualitative description and are


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All Office Staff Parking Noise Maintenance Safety Building Grounds Overall Nonrecommenders Office Staff Parking Noise Maintenance Safety Building Grounds Overall

9.6 9.7 17.3 17.5 16.5 21.5 15.6 19.4

15.5 13.8 27.7 28.9 27.1 34.9 25.9 33.3

62.3 40.8 38.6 38.5 38.4 37.8 30.1 52.0

2 Stars (%)

35.9 24.5 22.3 21.9 21.9 21.5 17.2 29.3

1 Star (%)

17.9 36.3 24.3 25.5 29.2 22.9 32.8 14.0

16.8 32.1 23.4 24.2 28.5 23.7 27.6 17.9

3 Stars (%)

3.8 7.5 8.0 4.7 3.9 4.1 10.2 0.7

15.1 17.4 22.2 14.0 15.1 22.5 26.2 16.2

4 Stars (%)

0.6 1.7 1.5 2.5 1.4 0.4 1.0 0.1

22.5 16.3 14.7 22.4 18.0 10.8 13.3 17.2

5 Stars (%)

62.4 38.4 42.2 44.2 42.5 48.1 31.3

43.9 40.1 41.8 37.0 40.0 48.1 34.9

Below Respondent’s Average Component Rating (%)

Table 2 Ratings Cross-Tabulation

23.0 47.2 41.9 39.2 40.7 34.6 52.5

39.5 43.5 40.5 45.1 41.9 32.9 46.8

Above Respondent’s Average Component Rating (%)

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0.8093*** 0.2805*** 0.4527*** 0.4197*** 0.5576*** 0.7151*** 0.5341*** 0.0054*** 0.0215*** 0.0588***

Office staff Parking Noise Maintenance Safety Building Grounds Rent ($100s) Bedrooms Bathrooms Intercept 5

Odds Ratio

0.0044 2.246 0.0039 1.324 0.0043 1.573 0.0047 1.522 0.005 1.746 0.0056 2.044 0.0052 1.706 0.001 1.005 0.0064 1.022 0.0098 1.061 –16.5613*** (0.0453) –13.5135*** (0.0408) –10.4324*** (0.0358) –7.6906*** (0.0329) 309,829

Standard Error

***p < .001. Note: Individual year dummy variables are included but not reported.

n (nonmissing values)

Intercept 2

Intercept 3

Intercept 4

Estimate

Variable

All Observations

2.227-2.266 1.314-1.334 1.559-1.586 1.508-1.536 1.73-1.764 2.022-2.067 1.689-1.723 1.003-1.007 1.009-1.035 1.04-1.081

95% Confidence Interval 0.5778*** 0.2142*** 0.3326*** 0.3508*** 0.4687*** 0.5311*** 0.3962*** 0.0067*** –0.0100 0.0491***

Estimate 0.0058 0.0051 0.0054 0.0059 0.0063 0.007 0.0062 0.0013 0.0085 0.0131 –15.1282 (0.1124) –12.3077 (0.0575) –8.4923 (0.047) –5.8052 (0.0438) 169,852

1.782 1.239 1.395 1.42 1.598 1.701 1.486 1.007 0.99 1.05

Odds Ratio

Nonrecommenders Standard Error

Table 3 Ordered Logistic Regression on “Overall” Rating

1.762-1.802 1.227-1.251 1.38-1.409 1.404-1.437 1.578-1.618 1.678-1.724 1.468-1.504 1.004-1.009 0.974-1.007 1.024-1.078

95% Confidence Interval

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not precisely equivalent to a number. An ordered logit model treats each subsequent response as a higher value (ordinal), without assuming that the distance between each value is identical. Consequently, studies evaluating residential satisfaction often use such models as an appropriate and reliable statistical technique (Barcus, 2004; Lu, 1999; Speare, 1974). The ordered logit model in this study indicates the probability that a respondent will give the apartment a greater number of “stars” in the overall rating category. Under this ordered logit model the cumulative probability of respondent, i, choosing a particular number of stars, j, or greater is = =

where pim is the probability that individual i chooses the star category m, and J is the highest category (in this case, five stars). Each Fij relates to a different division of the dependent variable (e.g., 2-stars or higher, 3-stars or higher, etc.). The model is then the J-1 set of equations −

=α +

=

−

where βxi = β1xi1 + … + Bkxik and k is the number of independent variables (Allison, 1999). Table 3 indicates that the office staff rating is the subcategory rating that most strongly influences the overall rating in both the data set as a whole and in the truncated data set including only those respondents who did not recommend the apartment. This model also controls for other variables such as the amount of rent, number of bedrooms, number of bathrooms, and ending year of the lease. Table 4 reports similar results from a probit model using a dummy dependent variable equal to 1 if the respondent would recommend the apartment to a friend, and 0 if not. We exclude the “overall” rating from this analysis, treating it as an endogenous variable resulting from the individual category ratings. Again, the parameter estimates indicate that the “office staff” category is the category with the greatest impact on the overall likelihood of the respondent recommending the apartment to a friend. The predicted probability is the integral of the area to the left of the predicted point on the standard normal curve where t ~ N(0, 1). β

π

−

−∞



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Table 4 Probit Analysis on Recommending Apartment Community Variable

Estimate

Office staff Parking Noise Maintenance Safety Building Grounds Rent ($100s) Bedrooms Bathrooms Intercept

0.5942 0.1794 0.3521 0.2341 0.3175 0.3708 0.1810 –0.0198 0.0736 –0.0890 –6.5789

Standard Error

Pr > Chi Square

0.0042 0.0042 0.0045 0.0049 0.0053 0.0058 0.0057 0.0011 0.0073 0.0113 0.0375

Environment and Behavior

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