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Adjusting the market value of coastal property for beach quality.

It is important for appraisers to account for all factors that affect property value. A significant characteristic of coastal property is the quality of the beach nearby. A key aspect of beach quality is beach width, which provides a combination of storm protection and recreational benefits for a property owner, thus enhancing property value. Obviously, appraisers need to consider such factors in the valuation of property. Yet it is often difficult to measure the impact of beach quality on property value. A model is presented here that allows for an adjustment of property value for beach width.

Three methods are commonly used in appraising real estate. The cost approach uses a calculation for the cost of reproducing the structure. Subsequently, accrued depreciation is computed and subtracted from the cost figure, and the land value (derived by an analysis of comparables) is added. A key failure of this method is that it does not adequately take into account present and future use of the property, and it is of no use in estimating the value of land. The capitalization or income approach involves discounting the net income stream produced by the property to present value at an appropriate interest rate. For obvious reasons this method is generally more suitable for commercial property than for residential real estate. The sales comparison approach (or market approach) requires the comparison of the property for sale with similar properties that have been sold or listed recently. In addition, the value of the subject property is marked down if negative attributes such as poor design, outmoded plumbing, or a deteriorated roof are present, and adjusted upward for positive characteristics such as a superior landscaped yard, a swimming pool, or nice shade trees.

The sales comparison approach is widely employed, relatively simple to use, and serves as a good check on property values determined by the other methods of appraisal. Several serious liabilities are associated with this method, though. According to Robert Semenow,(1) the sales comparison approach's main weaknesses are 1) a paucity of good comparables; 2) a focus on short-term values allowing temporary market fluctuations to distort long-term values; 3) scattered comparisons that tend to sometimes ignore, overstate, or understate special influences that affect values; and 4) practitioners who often make too few or inadequate inspections of comparable properties to acquire good or accurate comparisons.

This article presents a hedonic model, which circumvents some of the difficulties previously mentioned, thereby improving the quality of the sales comparison approach to property assessment. Specifically, the improvement comes through multiple regression analysis, which permits a better measure of the monetary contributions of each of the subject property's attributes. Because houses vary significantly from each other, this method of analysis, known as the hedonic technique, facilitates property appraisal.(2) The authors used standard data readily available to real estate professionals.

Numerous studies have used this process to examine the effects of different characteristics on property prices. Various location, structural, and neighborhood characteristics combine to determine the price of housing. For example, William Donnelly(3) considers the disutility of location in a floodplain and the effect of that disutility on property value. A recent study by Paul Wertheim et al.(4) examines how the distance to the beach, the view of the ocean, and the beach frontage, among other variables, are significantly related to the market value of beach lots.

We extend the analysis to the relationship between beach width and the market value of the property. Storm surge, a rise in the ocean above normal water level, results in flooding in low areas all along coastlines. A wider beach provides added property protection from storms, a benefit that is capitalized in the market price of property. Further, wider beaches provide protection to property owners near to but not directly on the beach. Any increase in beach width provides greater land protection and consequently increases property values. In addition, the wider the beach the greater the recreational benefits.


This study focuses on Garden City (GC) and Surfside Beach (SB), two cities that comprise part of a 60-mile stretch of South Carolina known as the Grand Strand. These two adjacent towns are south of the popular resort of Myrtle Beach and border the Atlantic Ocean. SB, a residential development dating back to the 1950s, has about 2.1 miles of coastline, while GC, a locality that has had significant growth after the 1950s, has a coastline 4.9 miles in length.

After examining various characteristics that might explain housing prices in this market area, the following model provided the best statistical results:


The symbols, definitions, mean values, and standard deviations of the variables are listed in Table 1. The dependent variable (i.e., the actual selling price of the house) was adjusted to 1983 prices by the national pricing index for housing. Selling price, location, and structural information, such as square footage, number of rooms, and structural age, were obtained from multiple listing catalogs and county tax records. Distance variables were derived from various area maps. Numerous visits to the test area were conducted to verify and supply information based on the actual viewing of the property.
TABLE 1 Variable Descriptions and Descriptive Statistics

Variable Standard
Definition Symbol Mean Deviation

Selling price of house SP 93,677 52,377

Age of house (years) AGE 13.37 10.08

House space (square feet) SQFT 1989.30 840.35

Number of bathrooms BTH 2.02 .83

Dummy variable
(1 = fireplace) FP .19 .39

Distance to nearest beach DBCH 1,785.60 1,217.10

Width of high-tide beach
(feet) WBHT 89.40 23.21

Width of high-tide beach x
distance to nearest beach DBHT 168,650 127,710

Distance to center of
Myrtle Beach (miles) DMB 9.99 1.97

Dummy variable
(1 = located on oceanfront) OCNF .09 .28

Dummy variable
(1 = view of ocean) OCNV .20 .39

Dummy variable
(1 = view of inlet) VIN .12 .33

Year of sale
1 (Jan. 1983) to 8 (Dec. 1990) YEAR 5.22 2.25

Dummy variable
(1 = dock) DOCK .06 .24

Dummy variable (1 = sold
after Hurricane Hugo) HUGO .12 .32

A series of 32 survey markers spaced along the shorelines of the two towns provides the high-tide beach width (WBHT) measurements. The nearest survey marker to a particular house serves as a measure of the beach width variable, and an indicator of the degree of the protection from storm damage for that particular property. All survey marker readings were provided by the South Carolina Coastal Council for spring 1989. The average width of beach for the sample was 89 feet, although there is significant variation in beach width along the shorelines of the two towns. The average high-tide width for SB was 96 feet, while GC beaches at high tide averaged 64 feet. Three hundred and eighty-five single-family homes that were sold between 1983 and 1991 in the GC/SB communities make up the sample. The average price for the sample is $93,677. Variables that might be correlated with beach width, such as an oceanfront location, a view of the ocean, and a view of the inlet, are also included.

Nine percent of the sample are located on the oceanfront (OCNF), and for these houses the average price was $170,430. Twenty percent of the sample have a view of the ocean (OCNV) and 9% of the sample have a view of an inlet (VIN) located behind a section of GC. Property owners located directly on the inlet have an opportunity to construct a dock (DOCK) that allows boat access to the ocean. Six percent of our sample have docks, which represent a valuable amenity.

Nineteen percent of the sample have fireplaces (FP), which represent proxies for enhanced amenity bundles. As an indicator of the value of living closer to a central business district (Myrtle Beach), DMB is included. The average distance to Myrtle beach is approximately ten miles. The year of sale (YEAR) is included to control for possible fluctuations in housing market conditions that affect sales. The sample is fairly evenly distributed over the eight-year period with slightly more than 10% of the properties sold in most years. At the date of sale, the average house in the sample was approximately 13 years old, contained 1,989 square feet of living space, and had 2 bathrooms.

The distance a property is from the beach (DBCH) has been shown to be inversely related to housing prices, thus providing a basis for measuring the recreational value of the beach.(5) To account for the recreational benefit from wider beaches, an interaction variable (DBHT) was created by multiplying DBCH by WBHT. Including only a distance variable would not capture the benefits of a wider beach.

A dummy variable (HUGO) was used to adjust for houses sold after Hurricane Hugo hit the area in 1989. Twelve percent of the homes in the sample were sold after Hugo hit the South Carolina coast, causing extensive damage. It is hypothesized that purchasers are more hesitant to buy a home following an example of the damage that can result from a major storm. This was the first major damage from a hurricane in South Carolina in recent years.


Ordinary least squares was applied to the cross-sectional data described previously. Because functional form is not known beforehand, we considered alternative models. A linear model implies that a housing bundle can be easily unbundled and repackaged, an unlikely assumption for real estate. Using a Box-Cox transformation process in the search for functional form led to a log-linear form.(6) Because this is a multiplicative model, the contribution to value of an attribute depends on the existing level of that attribute. For example, an additional foot of sand, when the beach is 100 feet wide, will contribute a different increment to value than when beach width is 50 feet.
TABLE 2 Regression Results for Single-Family Homes In Surfside Beach and
Garden City, South Carolina
(dependent variable = deflated selling price of single-family home in 1983

Variable Coefficient Error t-ratio

ONE 6.41036 .448113 14.305 .00000
AG -.028096 .010378 -2.707 .00703
SQFT .605672 .041430 14.619 .00000
BTH .129034 .039789 3.243 .00146
FP .045313 .032137 1.410 .15537
DBHT -.128541 .018790 -6.841 .00000
WBHT .262120 .039759 6.593 .00000
DMB .281101 .0944062 2.978 .00324
OCNF .237079 .0512959 4.622 .00001
OCNV .090229 .0494097 1.826 .06515
VIN .167454 .0532304 3.146 .00196
TIME -.025012 .0263492 -.949 .34557
DOCK .179018 .0582845 3.071 .00246
HUGO -.123804 .0405648 -3.052 .00260

N = 385; Adjusted [R.sup.2] = .81; F-value = 124.15

The hedonic model results are presented in Table 2, and as expected, beach width has a significant positive effect on the price of housing. The coefficients represent the marginal effect a particular characteristic has on house value, holding constant other variables in the model. All continuous variables are in logarithmic form. The model predicts well with an adjusted [R.sup.2] of .81, indicating that 81% of the variation in housing prices can be explained by the model. The column labeled "significance" indicates that all variables except YEAR,(7) FP, and OCNV are significant at the 1% level. OCNV is significant at the 10% level.

Since this is a log-linear model, the coefficients of the attributes listed in Table 2 represent elasticities. For example, the WBHT coefficient indicates that for an increase in beach width of 10%, the house value will increase by 2.6%. Adjustment for a comparable could be made by using these elasticities. For example, consider two houses that are similar in every respect except that the beach is 10% wider for house A than for house B (e.g., 110 feet versus 100 feet). If house B sold for $100,000, then house A must sell for $102,600 (2.6% more than B).

Clearly, the value of a wider beach is greater for property near the ocean than for property located farther back. For the two cities in our sample, the greatest distance from a house to the beach is one mile. Using mean values for houses at different distances, it can be shown that beach-width value diminishes as a house is farther removed from the ocean. For the average oceanfront house with an average market price of $170,430, a 10% increase in beach width of 7.9 feet will increase value by $4,431. For the average house one-half mile from the ocean with an average price of $77,567, a 10% increase in beach width of 9.3 feet would increase value by $2,017.

Although beach width strongly affects housing values in this study, coastal housing markets in other areas could have significantly different results. Dennis Bialaszewski and Bobby A. Newsome(8) find that floodplain location does not lower housing prices for all market areas. They maintain, therefore, that adjustments to comparable sales should be derived from the market in which the property is located. Similarly, housing markets that have more or less threat of erosion than this study area could show different results. Thus more research should be conducted before applying the results in this study to all coastal property.

Although this study is principally concerned with the impact of beach width on housing prices, several other variables are of interest for possible use as comparables. Most variables are significant with the expected signs. The interaction variable DBHT indicates that the farther houses are from the ocean the lower their value. As the beach widens, the differential between houses on the oceanfront and houses farther back increases. OCNF is strongly positive, indicating that the value of property increases as a result of being located on the ocean. A dock also adds significantly to property value.

DMB is significant and positive, indicating that the farther away a house is from Myrtle Beach the more expensive it is. Because the area of study is a resort area, the largest nearby city, Myrtle Beach, would not provide the traditional amenities of a central business district. Rather, this is a neighborhood proxy for the more valuable homes that populate the southern end of the sample area. HUGO is significant and negative, indicating that houses sold after Hurricane Hugo were valued lower.


The study demonstrates that a wider beach increases property value. Further, the hedonic model provides an estimation of how much property value increases as a result of a wider beach, thus providing values for the sales comparison approach. Beaches often change shape because of natural and man-made causes. Coastal storms can cause coastal erosion, which adversely affects the value of property. Further, development projects that destroy natural coastal protection, such as sand dunes, can cause erosion. On the other hand, beach nourishment projects improve beach quality and enhance property value. An estimation of the value of losses and gains is important to appraisers, real estate brokers, tax assessors, buyers, and sellers, especially in light of the fact that beach nourishment is becoming increasingly popular as a method of restoring and enhancing beach quality.(9) This study offers a multiple regression method of measuring the expected changes in property values.

1. Robert W. Semenow, Questions and Answers on Real Estate, 8th ed. (Englewood Cliffs, New York: Prentice-Hall, Inc., 1977), 436.

2. Sherwin Rosen, "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy (January-February 1974): 34-55.

3. William A. Donnelly, "Implicit Value and Risk Perception: Sales of Floodplain Property," The Real Estate Appraiser and Analyst (Winter 1988): 5-10.

4. Paul Wertheim, Jon Jividen, Dave Chatterhee, and Margaret Capen, "Characteristics that Affect the Market Value of Beach Lot Property," The Real Estate Appraiser (August 1992): 59-64.

5. See Steven F. Edwards and F. J. Gable, "Estimating the Value of Beach Recreation from Property Values: An Exploration with Comparisons to Nourishment Costs," Ocean and Shoreline Management (1991): 37-55.

6. For a discussion of functional form considerations and the Box-Cox method see William N. Weirick and Franklin J. Ingram, "Functional Form Choice in Applied Real Estate Analysis," The Appraisal Journal (January 1990): 57-73.

7. An alternative measurement for the year of sale was tested. This was a separate dummy variable for the year of sale of the house. These also were insignificant. The HUGO variable, measuring houses sold after 1989, is significant.

8. Dennis Bialaszewski and Bobby A. Newsome, "Adjusting Comparable Sales for Floodplain Location: The Case of Homewood, Alabama," The Appraisal Journal (January 1990): 114-118.

9. In fact, for the area examined in this case study, a planned nourishment project for 1995 would double the width of the beach.

James R. Rinehart, PhD, is professor of economics at Francis Marion University in Florence, South Carolina. He received a PhD from the University of Virginia, and his research interests include environmental economics and the economics of education. Mr. Rinehart has published widely.

Jeffrey J. Pompe, PhD, is assistant professor of economics at Francis Marion University. Mr. Pompe has previously published in several real estate-related journals. His research interests include coastal resource issues, labor contractual forms, and cultural economics, and he received a PhD from the Florida State University.
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Author:Rhinehart, James R.; Pompe, Jeffrey J.
Publication:Appraisal Journal
Date:Oct 1, 1994
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