On the Waterfront: Still the Most Valuable Location

 

 
On the Waterfront: Still the Most Valuable Location
In-Depth Analysis, January 17, 2006
By Paul Emrath, PhD
 
As anyone with experience in the real estate industry knows, the price that a new home commands in the marketplace can differ substantially depending on where the home is built. Customers are often willing to pay more for a home if it’s located near a major employment center or in a region with a favorable climate. Particular amenities or disamenities located near a home may also impact the home’s price. A developer who expends resources to add recreational amenities —such as a clubhouse or walking trails— to a community is doing so to increase the value of homes in the community. And a home located in a neighborhood with unattractive features such as abandoned buildings is likely to sell for less. This is true, even if the physical characteristics of the home itself do not change.
 
NAHB has investigated the impact of location on price several times in the past, [1] using a statistical model that estimates prices controlling for a variety of different features. New data have recently become available, and these have been used to recalibrate the NAHB model. This article uses the recalibrated model to estimate the impacts of both general location and specific neighborhood features on house price.
 
The results are broadly consistent with results published previously by NAHB. General location and a variety of neighborhood characteristics continue to have statistically significantly effects on house price. The characteristic with the largest negative impact on price is the presence of abandoned buildings in the neighborhood. The characteristic with the largest positive impact on price is a location on the waterfront.
 
 

The Model and Data
The NAHB model to estimate house prices uses data from the American Housing Survey (AHS), a nationally representative survey of about 60,000 housing units funded by the U.S. Department of Housing and Urban Development and conducted by the U.S. Census Bureau in odd-numbered years. The NAHB model is of the type usually called a “hedonic regression.”  In practice this means not much more than a conventional statistical model used to estimate the price of a commodity as a function of its characteristics, although there is a substantial academic literature that goes into the underlying theory. [2]
 
When estimating such a model, it’s useful to a have data on a large number of housing characteristics, and the AHS provides a large government data base with hundreds of variables. Ultimately, however, no data set can possibly capture all features that potentially affect price of a commodity as complex as a house; so there's always a chance that a particular feature in the model is acting partly as a proxy for others. For example, if homes with fireplaces tend to have higher quality decorative hardwood trim (a feature not available in the data), the estimated impact of a fireplace could be picking up part of the effect of the hardwood trim, as well as the value of the fireplace itself.
 
This shows up in the effect of the age of the structure. As in many other studies, the NAHB model finds that newer homes have higher prices, and that this effect remains strong no matter how many other physical and locational characteristics the model controls for. One possible explanation is that new homes have more of some characteristic (higher quality construction, more attractive floor plans on average, etc) not captured in any data set. Another is that homes require less maintenance in the early part of their service lives no matter when they are built, and that customers are willing to pay more up front for reduced maintenance costs. The explanations are not mutually exclusive. Both could be true to some extent.
 
In addition to providing data on a large number of characteristics, an advantage of the AHS is that it covers the entire country in a consistent manner —although this means that even with a sample of 60,000 units, it’s unable to provide results for small units of geography. Not much detail is available below the four principle Census regions.  Information is available for some metro areas (generally aggregations of contiguous counties based on inter-county commuting patterns), but there are generally too few observations in any one metro to treat it separately. It is possible, however, to carve out a number of the larger California metros and treat them as their own "region" separate from the rest of the West (Figure 1). [3]
 
Figure 1. The Five Principal Regions
 
Source: NAHB hedonic regression model for owner-occupied, single family detached homes, based on data from the American Housing Survey, U.S. Census Bureau and the Department of Housing and Urban Development.
 
Therefore, the NAHB model doesn’t estimate the price of a particular home in a specific neighborhood, but instead produces an average price for a single family detached home with particular characteristics across a broad Census region.
 
 

General Location
To illustrate the impact of general location on price, we look at the estimated price of a “standard” home in the various regions. For purposes of this article, a standard home is defined as one with the following characteristics:
• A new (built after 2003) single family detached home
• 1,850 square feet of living space
• 2 full bathrooms (no half bath)
• 3 bedrooms
• A dining room
• 2 miscellaneous rooms (i.e., rooms other than bed, bath, dining, or family—including a kitchen)
• A basement
• A garage
• A fireplace
• No special neighborhood amenity or disamenity

Most of these characteristics are based on national averages for new 2-bathroom homes. Two is the number of bathrooms most commonly encountered in new single family homes (accounting for 36% of completions in 2005, compared to 33% for 2 1/2-bath and 28% for 3 or more baths. (See Excel table
 
Some of characteristics, of course, vary from region to region.  Nearly 80% of homes built in the Northeast or Midwest have basements, compared to well under 20% in the South and West. (See Characteristics Model)
 
Readers who find the above definition of a standard home imperfectly suited to their needs are reminded that the NAHB model is available online, it can be used interactively to evaluate homes with different characteristics (see the paragraph at the end of the article).
 
In addition to the region, the AHS also identifies the metropolitan status (central city, suburb, and non-metro) of the area in which a home is located. Figure 2 shows the estimated price of a standard new home built in each region/metropolitan area combination. The estimated price is highest by far in the large California metro areas. Outside of this, there is a general tendency for the house price to be higher in the Northeast and West Census regions, as well as in central cities and suburbs. The price tends to be lowest if the home is built outside of a metro area, but there is enough regional variation so that the price for the house is higher in the non-metropolitan than if it were transplanted to an average central city or suburban location in the Midwest or South Census regions. At the extremes, the model estimates that the standard new home will cost more than $550,000 if built in one of the large California metro areas, but under $175,000 if built outside of a metropolitan area in either the Midwest or South.
 
Figure 2. Estimated Price of A Standard New Home by Location
Source: NAHB hedonic regression model for owner-occupied, single family detached homes, based on data from the American Housing Survey, U.S. Census Bureau and the Department of Housing and Urban Development.
 

 
Neighborhood Amenities
The NAHB model also controls for attributes of the neighborhood in which the home is located. As a result, developers can use the model to help evaluate potential building sites.  Home owners, community associations, and local governments could also use it to estimate how certain public policies (such as providing public transportation, or finding a use for abandoned buildings) are likely to impact home values in their neighborhoods.
 
Figure 3 gives an illustration, again using a standard new home built in a Midwestern suburb as the starting point. The figure shows that the neighborhood amenity with the largest positive impact on the price of the standard house is a location on a body of water (such as a lake, river or ocean). In the baseline case with no special locational amenity, the estimated price of the Midwestern suburban home is $212,137. Moving a similar home to an otherwise similar neighborhood on the waterfront increases this to $303,760. The neighborhood amenity with the second greatest impact, adequate public transportation nearby, only raises the estimated price to $238,340.
 
Figure 3. Estimated Price of a Standard New Midwest Suburban Home with Various Neighborhood Amenities
 
Source: NAHB hedonic regression model for owner-occupied, single family detached homes, based on data from the American Housing Survey, U.S. Census Bureau and the Department of Housing and Urban Development.
 
Because a home is such a complex commodity, there are many characteristics that can potentially have a significant impact on its price, so NAHB develops its price estimating model by time experimenting with a large number of the home and neighborhood features available in the AHS. In general, features that show statistically significant impacts on home value are retained in the model. Features not included are  checked to make sure that their removal doesn’t change other results materially to minimize omitted variable problems.  Beyond this, many interactions are examined, particularly interactions between region and neighborhood characteristics.
 
NAHB found that the positive impact of a waterfront location on house price was very powerful, irrespective of Census region or metropolitan status.  A body of water nearby raises the price of the standard home in a Midwestern suburb by 43 percent (or $92,000), the price of the standard home in the nonmetropolitan South by 44 percent (or $75,000) and the price of the standard home in a central city of one of the large California metro areas by 41 percent (or $243,000). The percentage increases are close but not identical due to the particular form of the NAHB model.
 
Price estimates for all amenity/region combinations are shown in Table 1. In some cases, the table shows a strong interaction between a particular amenity and the region in which the home is located. For example, the model shows that adequate public transportation in the neighborhood raises the price of the standard new home in a Midwest suburb from $212,137 to $238,340 (a $26,000 or 12% increase), while adequate public transportation raises the price of the standard new home in a Western (other than large California metro) suburb from $237,162 to $244,411 (only a $7,000 or 3% increase).
 
 
 
 
Neighborhood Disamenities
Neighborhood features may also be “disamenities,” in that their presence reduces the price of the home. The AHS provides information on several potential disamenities. Among them, the presence of abandoned buildings within one-half block (roughly 300 feet) of the front of the home turned out to have the greatest negative impact on price, reducing the price of the standard home in a Midwestern suburb from $212,137 to $188,805.  Bothersome trash, industrial buildings, inadequate shopping, and bad roads also had significant negative effects on the price of the home (Figure 4). Inadequate shopping is treated as a disamenity (rather than treating adequate shopping as an amenity) because the vast majority of homeowners in the AHS report having access to adequate shopping in their neighborhoods. Thus, adequate shopping is the normal condition, and inadequate shopping represents a relatively rare event, like the other disamenities in Figure 4.
Figure 4. Estimated Price of a Standard New Midwest Suburban Home with Various Neighborhood Disamenities
Source: NAHB hedonic regression model for owner-occupied, single family detached homes, based on data from the American Housing Survey, U.S. Census Bureau and the Department of Housing and Urban Development.
 
That a number of neighborhood disamenities turned out to have an easily quantified impact on house price is perhaps surprising. Because the presence of many of these disamenities—abandoned buildings and litter, for instance —are likely to be correlated with one another, we might have expected difficulty when trying to tease out the individual impacts; but the statistical relationships between these characteristics and house prices were very strong.

Again, the NAHB model captures interactions between general location and particular neighborhood features. The model shows that an abandoned building in the neighborhood lowers the price of the standard new home in a Midwest suburb from $212,137 to $188,805 (a $22,000 or 11% reduction). Although this is substantial, an abandoned building lowers the price of the standard new home in a Northeastern central city all the way from $280,741 to $189,001 —a more imposing $92,000 or 33% reduction.
 
 

Accessing the Model
The table and graphs in this article illustrate only a few of price estimates the NAHB model can generate. Interested readers are encouraged to explore other house feature/locational characteristic combinations themselves by accessing the model online. The model is available interactively in the form of the “NAHB Single Family Detached House Price Estimator”.
 
Running the house price estimator requires a computer with a reasonably recent version of Microsoft Excel. In addition, Excel’s security setting must be set to either “low” or “medium.”  Once this is done, it’s possible to access the NAHB model; specify a home’s age, features, location and neighborhood characteristics; and easily generate an estimated house price.
 
Technical details of the statistical model described in this article and used to generate the house price estimator are available from NAHB’s Housing Policy Department.
 
 
_________________________________
Footnotes:
 
[1] Most recently in “How Features of a Home Impact Its Price”  Housing Economics, December 2004: .
 
[2] The earliest such article usually cited is Waugh’s “Quality Factors Influencing Vegetable Prices” published in the Journal of Farm Economics in 1928. Articles by Zvi Griliches (“Hedonic Price Indexes for Automobiles: An Econometric Analysis of Quality Changes” in The Price Statistics of the Federal Government, National Bureau of Economic Research, 1961) and Sherwin Rosen (“Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition,” Journal of Political Economy, 1974) have been particularly influential.

[3] The metro areas included in the California region are Bakersfield, Fresno, Los Angeles-Long Beach, Modesto, Oakland, Orange County, Riverside-San Bernardino, Sacramento, San Diego, San Francisco, San Jose, Santa Barbara-Santa Maria, Santa Rosa, Stockton-Lodi, Vallejo-Fairfield-Napa, and Ventura.

For more information about this item, please contact Paul Emrath at 800-368-5242 x8449 or via email at pemrath@nahb.org.


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