Price Variation in Waterfront Properties Over the Economic Cycle

by

Randy E. Dumm

William T. Hold Professor of Risk Management and Insurance Department of Risk Management/Insurance, Real Estate, and Legal Studies

College of Business Florida State University

Tallahassee, Florida 32306-1110 (850) 644-7880 (Phone)

(850) 644-4077 (Fax) rdumm@business.fsu.edu

G. Stacy Sirmans

Kenneth G. Bacheller Professor of Real Estate Department of Risk Management/Insurance, Real Estate, and Legal Studies

College of Business Florida State University

Tallahassee, Florida 32306-1110 (850) 644-7845 (Phone)

(850) 644-4077 (Fax) gsirmans@business.fsu.edu

Greg T. Smersh

Assistant Professor Department of Finance

College of Business University of South Florida

Tampa, Florida 33620 (813) 974-6239 (Phone)

(813) 974-3084 (Fax) gsmersh@usf.edu

November 2014

Price Variation in Waterfront Properties Over the Economic Cycle

Abstract

Using sales data from the Tampa Bay housing market for the period from 2000 to 2012,

this paper examines price performance across the boom, bust, and post-bust phases of the most

recent real estate cycle by comparing waterfront properties to non-waterfront properties and by

comparing specific waterfront types. Waterfront properties enjoyed a 7.2 percent price premium

over non-waterfront properties but this premium was higher in the latter part of the boom stage

of the cycle and for the post-bust part of the cycle (2011 and 2012). When evaluating the

performance of specific waterfront types, properties on the bay, canal, lake, or river provided

price protection through the real estate cycle while the price performance of on-pond properties

in some years was more closely aligned with non-waterfront properties.

Key Words: waterfront, house prices, price premiums, economic cycle

Price Variation in Waterfront Properties Over the Economic Cycle 1. Introduction

The multidimensional nature of a residential property can make its valuation problematic.

Homeowners consume (and value) different sets of structural characteristics and amenities, while

a home’s immobility and proximity to various spatial characteristics (such as neighborhood

makeup and distance to city center) causes a locational effect. In addition, home values are

affected by their economic environment and cyclical behavior, of which a good example is home

price movement over the 2000s decade boom and bust cycle. An interesting question is the

extent to which these factors interact to produce observed property values. Within this context,

this study examines the movement in house prices based on these three factors: (1) different

structural characteristics, (2) spatial amenities (specifically waterfront versus non-waterfront),

and (3) the real estate market boom and bust of the 2000s decade.

Studies have confirmed that the value of residential real estate is a function of both physical

characteristics and location (see Sirmans, Macpherson, and Zietz (2005) for a review). Physical

characteristics such as square footage, age, and lot size can be easily measured and factored into

property values, and their directional effects on house prices are generally consistent across the

literature. Locational characteristics however, may be a combination of either positive or

negative factors and extracting value effects can be more ambiguous and problematic. For

example, the positive effect of waterfront location or a water view would have a positive effect

on property value whereas being surrounded by inferior homes or a deteriorating neighborhood

would have a negative effect. In addition, across the economic cycle, the price behavior of

properties with certain amenities (such as waterfront) may differ from that of properties lacking

these amenities, and this relationship is not clearly established or understood.

Waterfront location is further complicated by the fact that it can have two distinctly different

amenity factors. First, there are consumptive factors, or the recreational use for boating,

swimming, etc. which typically are associated with properties that are directly on the water.

Second, there are non-consumptive factors such as scenic views or perhaps a sea breeze that can

be related to properties that are either on the water or nearby. In areas with higher elevations,

this amenity can be realized for a significant distance from the water in some cases. Florida

however is very flat and in most situation, there is no view unless the property is directly on the

water. This makes the analysis of waterfront versus non-waterfront more straightforward, since

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both the recreational or scenic amenities is almost always limited to waterfront locations for

areas in Florida.

A recent study by Wyman, Hutchinson, and Tiwari (2014) provides some insight by

examining the pricing of waterfront view amenities in a South Carolina lakefront community

across the 2000s boom and bust cycle. This study considers the view amenity of vacant lots for

lakefront properties and finds price premiums vary significantly across different quality of

waterfront views. Additionally they find that the premium for waterfront view is affected by the

2000s decade recession, with higher quality waterfront properties being better protected over the

bust period. Gordon et al. (2013) also examine the pricing of waterfront view amenities by

considering the impact of locational preferences and externalities on condominium unit prices

and find that buyers are willing to pay more for the view in terms of elevation and corner

location even if considering negative externalities associated with these properties.

Although these results are informative and useful, our study provides a more comprehensive

analysis of the price effects of different types of waterfront for developed properties (not just

vacant lots) over the 2000s decade boom and bust period. Thus, our study has the advantage of

measuring not only price premiums for properties located on different types of water (bay, river,

canal, lake, and pond) but also the change in these waterfront prices over time in different

economic conditions.1 Using Tampa, Florida bay area data and examining the period 2000

through 2012, the results show an overall 7.2 percent price premium over non-waterfront

properties. Average price premiums across the 12 year time period vary greatly by type of

waterfront: bay (107%), river (62%), canal (61%), lake (15%), and pond (3.1%).

Evaluation of the performance of specific waterfront types across the economic cycle shows

that the premiums were highest at the end of the boom stage of the cycle (2006-2007) and at the

end of the recovery stage of the cycle (2011-2012). Properties on bay, canal, lake, or river

provided superior price protection through the real estate cycle relative to non-waterfront

properties. When the waterfront property types are segmented by premium and non-premium

waterfront, further differences in capitalization effects are observed. Price premiums vary

greatly from year to year over the 2000 to 2012 period. The bay premium peaked at 177 percent

in 2005. The canal premium reached its highest level at 80 percent in 2005 while the river

premium peaked in 2008 at 79 percent. The lake premium peaked at 22 percent in 2007 and the

pond premium reached its highest level at 5 percent in 2005. Price premiums decreased for all

types of waterfront during the bust period; however by the post-bust period all the waterfront

types had seen an increase in premiums.

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2. The Value of Waterfront Properties

Previous research has established that spatial amenities such as waterfront location are

capitalized into property values (e.g., Henderson (1977) and Diamond (1980)). For example,

Garrod and Willis (1994) find that waterfront location adds a premium of 3-5 percent to house

prices. Luttik (2000) finds that houses overlooking water sold for premiums of 8-10 percent.

Other studies (e.g., Garrod and Willis (1994), Lansford and Jones (1995), and Rush and

Bruggink (2000)) have strongly supported the existence of a significant price premium for

waterfront housing for both consumptive (i.e., recreation) and non-consumptive (i.e., view) uses.

Studies also have shown that the value of water proximity has distance-decay effects. Thus,

most studies measuring the value of waterfront have utilized either distance gradients or discrete

measures of distance. In some cases, distance bands about an externality (e.g., Waddell, Berry,

and Hoch (1993) have been shown to offer results superior to the distance gradient approach.

For example, Archarya and Bennett (2001), looking at 0.25 mile and one mile radii, find selling

price to be negatively correlated with distance to the ocean and lakes. However, in other studies

the distance gradient approach provides superior results. For example, Lansford and Jones

(1995) find that waterfront commands a premium but that the marginal value decreases rapidly

as distance from the water increases.

A large percentage of prior research regarding waterfront amenity value has focused on the

aspect of waterfront view. Bourassa, Hoesli and Sun (2004) provide a review of studies that

examine the impact of views on residential property prices and find that the impact of a view is

greatest at the waterfront. Benson, Hansen, Schwartz, and Smersh (1998) find that the

willingness is quite high to pay for the amenity of a waterfront view. The authors find that

highest-quality ocean views increased the market price of an otherwise comparable home by

almost 60 percent and that the value of a view was found to vary inversely with distance from the

water. Conversely, Bourassa, Hoesli and Sun (2005) find that the increase in value for a

property with a water view was much less at about 10 percent. For lake views, Seiler, Bond, and

Seiler (2001) examine the impact of water views on property values for properties located on

Lake Erie. They find that having a lake view increased home values by about 56 percent. In a

follow-up study, Bond, Seiler and Seiler (2002) find that a lake view increased the property price

by nearly 90 percent. Doss and Taff (1996) find that a lake view increased property value by

about 44 percent.2

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3. The Data

This study analyzes single-family detached home sales in Hillsborough County, Florida over

the period 2000 to 2012. Hillsborough County’s 2010 population was just over 1.2 million

people and the county’s population density was over 1,000 people per square mile.

Approximately one-third of the houses in the sample are inside the city of Tampa and about 65

percent are owner-occupied. Even though only a relatively small portion of Hillsborough

County makes contact with the Gulf of Mexico (Tampa Bay), roughly 17 percent of the county's

total 1,266 square miles is water. Sales of waterfront properties average about five percent of all

sales.

The data are provided by the Hillsborough County Property Appraiser’s Office and include

heated square footage, year built, and the last three sales prices and dates. All sales in the final

sample are verified as qualified sales or “arms-length” transactions. All non-qualified sales

(such as purchase by a relative, foreclosure, or any other non-arms-length transaction) were

deleted from the sample. Supplementary property characteristics include the number of

bedrooms, the number of bathrooms, and the number of stories, garage, fireplace, pool, air

conditioning, building condition, type of exterior wall, type of interior flooring, and type of roof

cover. Binary variables represent the years 2001 to 2012.

The Hillsborough County Property Appraiser’s Office maintains a property line file that

indicates if certain features border a property; these include industrial use, commercial use,

farmland, water, major highway, etc. Further, the codes for property lines that are bordered by

water are disaggregated into several types of waterfront border codes, including bay-front (on

Tampa Bay), canal-front, river-front (primarily on the Hillsborough River), and lake-front. The

Hillsborough River arises from the Green Swamp near the northeast corner of Hillsborough

County and flows roughly 50 miles through the county to Tampa Bay. The property code file

also identifies other rivers; including the Alafia River, Little Manatee River, Manatee River,

Palm River, and Thonotosassa River. Hillsborough County also contains well over 100 lakes;

however, most of these are relatively small. Thus, the property border codes provide the

advantage of being able to identify the location of residential properties on different types of

waterfront.

Detailed GIS analysis was used to verify the property border codes and revealed a number of

errors. Many waterfront properties were not coded and some properties coded as waterfront

were in fact not on the water. To create a dependable database, a GIS polygon layer representing

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all water bodies in the county was created. Parcels for single family houses were selected and

exported from the Hillsborough County parcel layer to a new GIS layer. In order to better

organize the data, a file geodatabase was created for all the GIS data (water polygons and

property parcels).

A spatial query was made to select all the parcels that intersect the boundaries of the nearby

water bodies. A separate point GIS layer was created to represent these parcels. However, many

properties were still not selected even though they appeared to be (or were coded as) waterfront

properties. Many properties that are separated from a water body by a narrow road or land were

also not selected. To ensure a better representation of the waterfront designation, fifty-foot

buffers were created around all water bodies and a spatial query was made to find parcels that

intersect those buffers (instead of finding parcels that intersect water bodies directly).

Areas of all water bodies were calculated in acres, and all fields containing water

information (name, area, classification etc.) were transferred to the parcel layer. To create

classifications based on waterfront type, a tabular joined table was made between the parcel layer

and the table of waterfront property codes from the Hillsborough County Property Appraiser's

office. Properties were then identified with their correct waterfront types. GIS was also used to

create a distance variable from the coastline of Tampa Bay to all houses, providing for the use of

a single distance gradient. Hillsborough County is very flat, and in most cases, there is no scenic

view unless the home is located directly on the water. This distance variable was measured as

distance from Bay access rather than simply distance as the crow flies.

Additionally, GIS was used to dis-aggregate different waterfront types. Canal-front

properties were differentiated by access to Tampa Bay, as determined by bridges that would not

allow a sailboat mast to past under. Premium canal indicates that a sailboat can be docked there

and that it can motor out to Tampa Bay. River-front properties were differentiated by the level

of scenic frontage and the Alafia River, Palm, and Little Manatee Rivers were selected as having

a “wild and scenic” characteristic which we presume will command a price premium over

properties on other rivers. Lakes were differentiated by size. In a typical market, waterfront

property is valued not only for the view but also for the bundle of amenities that includes

swimming, sailing, water skiing, jet skiing, and fishing. As such, houses on lakes over 100 acres

were identified as premium lake properties. Ponds usually describe small bodies of water,

generally smaller than one would require a boat to cross. Some regions of the US define a pond

as a body of water with a surface area of less than 10 acres, and in this study, we classify ponds

based on this definition.3

5

GIS was also used to identify different positive and negative amenities located in close

proximity of residential properties. For example, mobile home parks were built along rivers and

lakes many years ago in Hillsborough County – these would be expected to negatively influence

single family property values. Department of Revenue (DOR) codes identify mobile home parks

as excellent, good, average, and below average. In addition to mobile home parks, we

investigate the influence of amenities such as golf courses, parks, cemeteries, and fitness centers.

4. Methodology

Real estate research has typically used hedonic regression analysis to measure the marginal

effects of housing characteristics on house prices. This provides a superior methodology for

investigating property submarkets (such as waterfront) which may have characteristics that are

significantly different from general market averages. A review by Sirmans, Macpherson and

Zietz (2005) of over 125 real estate studies that have used hedonic pricing models shows that

many types of variables have been included in these models. Such variables often include the

number of bedrooms, bathrooms, stories, as well as the existence of amenities such as garages,

pools, and fireplaces. All of those variables are included in this study. Additionally, this study

includes variables that indicate the existence of central heat and air, superior construction,

superior exterior wall, superior flooring, or superior roof cover. However, very few studies have

included direct measures of waterfront location. This study not only includes a measure of

location on the water, but further discerns between different types of waterfront.

The basic form of the hedonic pricing model is:

ln(sp) = α0 + βi Xi + εi

where selling price (sp) is expressed in logged form, α0 is a constant term, βi is the regression

coefficient for the ith housing characteristic, Xi is a vector of structural housing characteristics,

and εi is the residual error term. The model is expanded to include binary variables representing

several different types of waterfront and binary variables for the years 2001 to 2012, with 2000

as the base year.

With these additional variables, the hedonic model becomes:

ln(sp) = α0 + βi Xi + Ω Water + π Time + εi

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where Water is the designation of type of waterfront for a given property and Time is a vector of

binary variables indicating the property’s year of sale. A property may be designated as being

located on one four types of waterfront. Specifically, waterfront locations include on bay (on

Tampa Bay), on canal (man-made canals with access to Tampa Bay), on lake, and on river. If

being on the water is valued by consumers, houses built located on water should sell for higher

prices relative to houses not located on any water, other things constant. Having multiple

measures of waterfront type gives the advantage of being able to isolate the differential pricing

effects of these various types.

5. Results

5.1. Summary Statistics

The variables included in the regression model are defined in Table 1 and summary statistics

are provided in Tables 2 and 3. As shown in Table 2, houses that sold between 2000 and 2012

had an average selling price of $214,740 with an average square footage of 1,959. The average

lot size was .28 acres and the average number of stories was 1.2. The average age was 19.72

years and the average number of bathrooms was 2.23. Thirty percent of houses had a swimming

pool, twenty-two percent had a fireplace and over seventy-five percent of houses had a garage.

Almost all houses had central heating and cooling and a majority of houses (69.3%) had exterior

walls in the superior category or superior grade flooring (32.3%). A much smaller percentage of

houses were classified as superior based on the quality of construction (7.1%) or roof covering

(5.2%).

[INSERT TABLE 1 HERE]

The primary focus on this paper is on the value capitalization of waterfront location.

Overall, 16.9 percent of houses were located on some type of water. Of the waterfront

properties, a small number were located directly on the bay (194 or .1% of the sample). The

largest category of waterfront properties were properties located on a pond (30,809 or 14% of the

sample. The next largest category was lakefront properties (2,897 or 1.4% of the sample).4

River-front properties (636 or .2% of the sample) and canal-front properties (1,673 or .7% of the

sample) were the remaining two categories.

[INSERT TABLE 2 HERE]

We would expect to see price differences between homes that are located on some type of

water versus homes that are not. These differences could be a function of several factors,

including the waterfront location, other spatial amenities, and differences in structural

characteristics. Table 3 provides a means comparison between waterfront properties and houses

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off the water. All housing characteristics for waterfront properties are significantly superior to

those for non-waterfront. On average, the purchase price for waterfront properties was $85,064

more than that paid for property off the water. This price differential reflects differences in age

(waterfront properties newer by almost 11 years) and size (waterfront properties were just over

22% larger, over 424 square feet larger on average). Waterfront properties also had more

bedrooms and bathrooms, larger lot size, and were more likely to have a pool or garage.

Additionally, waterfront properties were more likely to be categorized as superior regardless of

category.

[INSERT TABLE 3 HERE]

Since the focus of this paper is not just on the impact of waterfront location but also on how

specific types of waterfront locations are capitalized into house prices, waterfront locations are

categorized as either on the bay (OnBay), on a canal (OnCanal), on a river (OnRiver), or on a

lake (OnLake). Table 4 and Figure 1 provide information on average prices for each specific

waterfront location. Not surprisingly, OnBay properties were the most valuable with an average

selling price of over $1.39 million. OnBay properties also had the greatest variation in price over

the decade. OnCanal properties had the next highest price with an average selling price of

almost $523,000 and the next highest variation in price over the decade. The average price for

OnLake properties ($375,000) was higher than the average price for OnRiver properties

($338,000) and the average selling price for these properties was much more stable over the

decade. As noted above, the least valuable of the specific types of waterfront properties was

OnPond properties with an average price of almost $254,000.

Table 4 provides additional price performance information for OnCanal, OnRiver, and

OnLake property types based on whether the waterfront location is classified as premium or non-

premium.5 Canal properties with bay access sold for more double the price for canal properties

that do not have bay access ($540,234 versus $250,408). Likewise, premium riverfront properties

(Alafia, Palm or Little Manatee Rivers) and premium lakefront properties (lakesize of 100 acres

or more) also show significantly higher prices with differences of over $76,000 for riverfront

properties and $228,000 for lakefront properties.

[INSERT TABLE 4 AND FIGURE 1 HERE]

5.2. Regression Results

The hedonic model is first estimated for the full sample and then for waterfront properties

only. Tables 5, 6, and 7 provide regression results for three distinct models.6 The R2 for these

models ranges from .7797 to .8460. The three models in Table 5 evaluate the impact of

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waterfront property and progress from measuring strictly on or off water, to considering the

specific types of waterfront, to also considering the effect of specific types of waterfront in the

context of other spatial variables. Model 1A, using the indicator variable OnWater, is a basic

model measuring the general effect of having a waterfront location. Model 1B expands the base

model by replacing OnWater with specific water types: OnBay, OnCanal, OnRiver, OnLake and

OnPond. Model 1C provides a further expansion by adding spatial variables such as distance

from city center, proximity to parks, fitness centers, cemeteries, golf courses and mobile home

parks.

Models 2A and 2B in Table 6 provide a further segmentation of waterfront properties by

differentiating between premium waterfront and non-premium waterfront. Model 2A measures

the price effect of premium versus non-premium waterfront types relative to being off-water.

Model 2B uses only waterfront properties to measure the price effect of specific waterfront

property types relative to properties on the bay.

[INSERT TABLE 5 HERE]

The models in Table 5 behave as expected.7 The building characteristics of square footage,

number of baths, number of stories, swimming pool, fireplace, and garage all have a positive

effect on selling price. The negative sign on bedrooms indicates that, holding square footage

constant, an additional bedroom has a diminishing marginal effect on value. Additionally, all

five of the superior housing categories have a positive effect on selling price. The variables

Y2001 through Y2012 represent the year in which the house is sold. With Y2000 as the omitted

year, the results show that house prices increased 100% (coefficient of 0.6936) through 2006

after which prices fell into a steady decline.

5.3. Waterfront Premiums

For Model 1A in Table 5, the key variable of interest, OnWater, is positive and significant.

Thus, without distinguishing between the different types of waterfront or considering any other

spatial effects or amenities, the price premium for waterfront properties is 7.2 percent

(coefficient of .0697).8

In Model 1B, the single waterfront measure is expanded to identify specific waterfront

types. The results show that the price premiums vary greatly based on the type of waterfront.

OnBay has the highest premium (115 percent), followed by OnCanal (62 percent), OnRiver (47

percent), OnLake (11.3 percent), and OnPond (3.5 percent). Thus, although a price premium

exists for being on any water, the market was clearly distinguishing between the different types

of waterfront.

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Model 1C builds from Model 1B by adding spatial variables.9 As the results show, selling

price decreases as distance increases from the central business district. Distance from the bay

district and being with 100 yards of a golf course have no effect on price. As the results in Table

1 indicate, the quality of the mobile home park proximity impacts sales price. As seen with

MHParkExcellent, which measures proximity to a high-quality mobile home park, closer

proximity has a positive effect on price. The lesser quality mobile home parks have the reverse

effect. The coefficients for MHParkGood and MHParkAVG, which measure second-tier and

third-tier quality mobile home parks, show that closer proximity has a negative effect on price.

The coefficient for the lowest quality mobile home park, MHPark<Avg is not significant,

although the direction of effect is negative.

Proximity to a recreation park has a negative effect on price. This is likely due to the

increased traffic created by the park and the resulting increased safety concerns. Being in

proximity to a cemetery does not have a significant effect on prices. Being in proximity to a

fitness center has a positive effect on price.

As seen in Model 1C, inclusion of the other spatial variables has no effect on the premiums

for OnBay (107 percent) and OnCanal (61 percent). However, there are differences for the other

waterfront types. Including the other spatial variables has a dramatic effect on the premium for

OnRiver which increases to 62 percent. The OnLake premium increases to 15 percent while the

OnPond premium decreases to 3.1 percent.

Table 6 provides a further dissection of waterfront and a more in-depth analysis by

distinguishing between premium and non-premium waterfronts. In Model 2A, lakefront is

segmented into premium lakefront and non-premium lakefront, riverfront is segmented into

premium riverfront and non-premium riverfront, and canal is segmented into premium canal and

non-premium canal. In addition, smaller bodies of water are distinguished as ponds.

As the results show, the premiums for OnBay and OnPond are not affected since they have

no premium or non-premium categories. The difference between premium and non-premium

river is minimal (61.8 percent compared to the premium for a non-premium river at 61.5

percent). There is a slightly greater difference between premium and non-premium canal (61

percent compared to 58.1 percent). The biggest differences across premium versus non-premium

waterfront property types is with lakefront properties. Premium lakefront shows a 25 percent

premium while non-premium lakefront is 11.3 percent.

Model 2B in Table 6 estimates the hedonic model using only waterfront properties and

further validates the results of Model 2A. The results show that all other waterfront properties

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have lower price premiums than Bayfront properties and the reductions are consistent with the

results shown in Model 2A.

5.4. Waterfront Premiums over Time

Single-family home sales activity varied greatly from year to year over the 2000s decade,

from just over 18,000 qualified sales in 2000 to more than 27,000 qualified sales in 2005. The

number of sales decreased dramatically in 2007 to 10,631 qualified home sales. Sales activity

rebounded somewhat, increasing to 12,264 qualified sales in 2012. The average house price

doubled from just under $150,000 in 2000 to over $300,000 in 2007, and then dropped to just

over $197,500 in 2012.

To investigate waterfront property performance over the 2000s decade real estate cycle, the

hedonic model is estimated by year. Table 7 provides the yearly regression coefficients for the

OnWater variable and the other primary waterfront variables (OnBay, OnLake, OnRiver,

Oncanal, and OnPond) and Table 8 shows the price premiums associated with these coefficients.

As seen in Table 8, the price premium for OnWater properties increased from 7.29% in 2000 to a

high of 10.17% in 2005. The price premium fell to a low of 2.76% in 2008 but it increased

substantially since then to reach its highest point in 2012 at 10.95%. Table 8 also shows a

significant premium exists for OnBay properties even before the “irrational exuberance” of the

early-mid 2000s. As the market became more active, the premium for OnBay properties

increases until 2005. The premium then decreases until 2009 when it is approximately equal to

2000 premium. As the market begins to recover, the premium increases until, in 2012, it is about

two-thirds higher than the 2000-level premium.

OnLake properties show variation in the annual premium but the premium is not on a steady

upward trend. Between 2000 and 2005, the highest premium in a given year was in 2005. The

premium then decreases in 2006 but peaks again in 2007. From there the premium decreases

annually until it increases sharply in 2012. The OnRiver premium shows a steady increase from

2000 to 2005. The premium decreases in 2006 but peaks again in 2008, drops in 2009 and then

increases steadily through 2012. The OnCanal premium increases steadily through 2005, then

decreases in 2006 and holds a somewhat even pattern through 2012. The OnPond premium

peaks in 2005 but is relatively even over the entire period 2000 through 2012.10

6. Summary and Conclusions

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While the positive effect that waterfront location has on residential property values is

intuitive, little research has been done to examine the performance of waterfront versus non-

waterfront properties over time, or whether performance differences exists between different

types of waterfront. Using sales data from the Tampa Bay housing market for the period from

2000 to 2012, this paper has examined the variations in capitalization rates for different types of

waterfront properties and the price performance of housing across the boom, bust, and post-bust

phases of the most recent 2000s decade real estate cycle. Additionally, the price effects of

various positive and negative externalities including golf course property and properties near

cemeteries, parks, and fitness centers was examined.

The results showed that waterfront properties, on average, enjoyed a 7.20 percent price

premium over non-waterfront properties. This premium was shown to be higher in the latter part

of the boom stage of the cycle and for the post-bust part of the cycle (2011 and 2012). The price

premiums were shown to vary greatly by type of waterfront. OnBay properties had an average

premium of 107 percent over the time period while OnCanal and OnRiver had average premiums

of 61 percent and 62 percent, respectively. OnLake and OnPond premiums averaged much

lower at 15 percent and 3.1 percent, respectively. When waterfront properties are distinguished

between premium and non-premium waterfront, further differences in capitalization effects are

observed.

Price premiums were seen to vary greatly from year to year over the 2000 to 2012 period.

The OnBay premium reached its highest level at 177 percent over non-waterfront properties in

2005. The OnCanal premium reached its highest level at 80 percent in 2005 while the OnRiver

premium reached its highest level at 79 percent in 2008. The OnLake premium reached it

highest level at 22 percent in 2007 and the OnPond premium reached its highest level at 5

percent in 2005. Price premiums decreased for all types of waterfront during the bust period;

however by the post-bust period all the waterfront types had seen an increase in premiums.

12

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1

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2

End Notes 1. Comparing waterfront non-waterfront properties is somewhat analogous to a recent study by

Dippold, Mutl, and Zietz (2014) that examines investors’ decisions to green-certify a

property. They find that the decision to green-certify a building is responsive to both

economic conditions (just as we found that waterfront properties are sensitive to changes in

economic conditions over time) and the attitudes of the local population (just as we found

that prices are affected by the tastes and preferences of the local population).

2. Disparate results in previous literature are likely due to differences in data collection,

modeling techniques, and the motivation for the research (i.e., view benefits versus

recreational benefits). For example, Benson, et al. (1998) collected data by driving around

the area, and focused solely on view characteristics.

3. There is no "official" definition of pond or lake. Pond usually describes small bodies of

water, generally smaller than one would require a boat to cross. Also, lakes tend to have

much more irregular shorelines, with coves and so forth, while ponds generally allow one to

take them all in visually from a single location. Given the lack of other amenities for these

small bodies of water (e.g., recreational boating) we would argue that the value added is

likely limited to the water view.

4. The terms properties and houses are used interchangeable in this paper as the data set only

contains only single family residential real estate sales (i.e., land only sales are not

included).

5. Canal properties are classified as premium if the canal provides access to Tampa Bay. Lake

properties are classified as premium if the size of the lake is greater than or equal to 100

acres. The Alafia, Palm and Little Manatee Rivers are classified as premium rivers. OnBay

and OnPond properties do not have premium or non-premium classifications.

6. The Breusch-Pagan test was used to test the hypothesis of equal variances. Based on the test

statistic, the hypothesis was rejected (indicating the presence of heteroskedacticity). The

standard errors in Tables 5, 6, and 7 are now heteroskedasticity-consistent (HC3) following

McKinnon & White, Hayes & Cai.

7. Variance inflation factors (VIFs) were calculated for all the regression models in Tables 5

and 6. The main models (1A-C and 2A) show no indication of a multicollinearity issue

(VIFs <4.5 and mean VIFs between 1.4 and 1.9 for these models). Model 2B (onwater

property only) does show higher VIFs for the variables OnPond and NPLake. Given the

3

purpose of Model 2B and consistency of the primary results there with the other models, we

do not believe that multicollinearity is of significant concern in that model.

8. Coefficients are converted to premiums with the standard (ex-1) calculation.

9. Moran’s I statistic was calculated to evaluate spatial autocorrelation in the error terms. These

were aggregated in one-square-mile sections as the geographic level of aggregation, with

connectivity based on queen's case (edge-to-edge and vertex-to-vertex) adjacency. While

spatial dependence existed in basic models (containing no spatial variables), it was negligible

in the models shown here which incorporated distance and other spatial variables such as

waterfront location and proximity to various externalities. As such, we do not believe that

spatial dependence is of significant concern in these models.

10. Due to an insufficient number of observations on a yearly basis for several of the premium

versus non-premium water type subcategories, yearly models are not produced to validate

Models 2A and 2B. Test for statistical significant differences between the yearly coefficients

for premium versus non-premium water types indicated a statistical difference in five of

twelve years for premium lakes versus non-premium lakes and for premium canals versus

non-premium canals. There was only one year where there the difference between premium

river and non-premium river coefficients was statistically significant.

4

Table 1

Variable Definitions Variable Definition Ln(sp) Log of sale price ln(sp) = dependent variable Age Age of house at the time of sale SqFt The square footage of the house Lotsize The size of the lot in acres. Bedrooms Number of bedrooms Baths Number of bathrooms Stories Number of stories Pool Binary variable with a value of one if the house has a pool,

zero otherwise Fireplace Binary variable with a value of one if the house has a fireplace,

zero otherwise Garage Binary variable with a value of one if the house has a garage,

zero otherwise ACSuperior Binary variable with a value of one for central heating and

cooling, zero otherwise ConstSuperior Binary variable with a value of one for good or excellent

construction rating, zero otherwise ExtWallSuperior Binary variable with a value of one for superior grade exterior

wall, zero otherwise FlooringSuperior Binary variable with a value of one for superior grade flooring,

zero otherwise RoofSuperior Binary variable with a value of one for superior roof cover,

zero otherwise OnWater Binary variable for property on the water equals one, zero

otherwise OnBay Binary variable for property on the bay equals one, zero

otherwise OnCanal Binary variable for property on a canal equals one, zero

otherwise PremCanal Binary variable for property located on a canal with access to

Tampa Bay equals one, zero otherwise. NonPremCanal Binary variable for property located on a canal without bay

access equals one, zero otherwise OnRiver Binary variable for property on a river equals one, zero

otherwise PremRiver Binary variable for property located on the Alafia, Palm or

Little Manatee Rivers equals one, zero otherwise NonPremRiver Binary variable for property located on rivers other than Alafia,

Palm or Manatee Rivers equals one, zero otherwise OnLake Binary variable for property located on a lake equals one, zero

otherwise PremLake Binary variable for property located on a lake greater than or

equal to 100 acres in size equals one, zero otherwise

5

NonPremLake Binary variable for property located on a lake less than100 acres in size equals one, zero otherwise

OnPond Binary variable for property on a pond equals one, zero otherwise

CBD_Distance Distance from the property to the central business district BAY_Distance Distance from the property to the bay district GolfCourse_100yd Property is located within 100 yards of a golf course equals

one, zero otherwise MHParkExcellent Mobile home park rated as excellent equals one, zero otherwise MHParkGood Mobile home park rated as good equals one, zero otherwise MHParkAvg Mobile home park rated as average equals one, zero otherwise MHPark<Avg Mobile home park rated as below average equals one, zero

otherwise RecPark Property is located within one-half mile of a recreation park

equals one, zero otherwise Cemetery Property is located with one-half mile of a cemetery equals

one, zero otherwise Fitness Property is located with one-half mile of a fitness center equals

one, zero otherwise Y2001 – Y2012 Time trend variables for the years 2001 through 2012 (Y2000

is the omitted year)

6

Table 2

Descriptive Statistics (N=214,326)

Variable Mean StdDev Min Max Price 214740.40 165878.00 10500 6000000 Age 19.715 21.539 0 125 SqFt 1959.485 784.534 770 9921 Lotsize .278 .470 0 40 Bedrooms 3.347 .830 1 9 Baths 2.236 .745 1 10 Stories 1.221 .419 1 5 Pool .295 .456 0 1 Fireplace .219 .413 0 1 Garage .755 .430 0 1 ACSuperior .990 .099 0 1 ConstSuperior .071 .257 0 1 ExtWallSuperior .693 .461 0 1 FlooringSuperior .323 .467 0 1 RoofSuperior .052 .222 0 1 OnWater .169 .375 0 1 OnBay .001 .030 0 1 OnCanal .008 .088 0 1 PremCanal .007 .085 0 1 NonPremCanal .000 .022 0 1 OnRiver .003 .054 0 1 PremRiver .001 .031 0 1 NonPremRiver .002 .044 0 1 OnLake .014 .115 0 1 PremLake .004 .064 0 1 NonPremLake .009 .097 0 1 OnPond .144 .351 0 1 CBD_Distance 10.757 4.569 1 27 BAY_Distance 7.033 4.756 0 26 GolfCourse_100yd .056 .231 0 1 MHParkExcellent 3.627 2.130 0 13 MHParkGood 4.221 2.150 0 14 MHParkAvg 3.811 2.520 0 12 MHPark<Avg 1.511 1.133 0 8 Cemetery .020 .139 0 1 Fitness .195 .396 0 1 RecPark .084 .278 0 1

7

Table 3

Means Comparison of Housing Characteristics: On and Off Water Properties

(N=214,326)

Variable Mean

OnWater=1 Mean

OnWater=0

Diff

Sig Price 283704.70 198640.90 85063.80 *** Age 10.96 21.49 -10.53 *** SqFt 2312.11 1887.80 424.31 *** Lotsize .36 .26 .10 *** Bedrooms 3.62 3.29 .32 *** Baths 2.54 2.17 .36 *** Stories 1.32 1.20 .12 *** Pool .40 .27 .13 *** Fireplace .22 .22 .00 Garage .92 .72 .20 *** ACSuperior 1.00 .99 .01 *** ConstSuperior .04 .08 -.04 *** ExtWallSuperior .85 .66 .19 *** FlooringSuperior .30 .33 -.02 *** RoofSuperior .12 .04 .08 *** CBD_Distance 12.58 10.39 2.19 *** BAY_Distance 7.13 7.01 .11 *** GolfCourse_100yd .08 .05 .03 *** MHParkExcellent 4.00 3.55 .45 *** MHParkGood 4.32 4.20 .12 *** MHParkAvg 4.45 3.68 .77 *** MHPark<Avg 1.94 1.43 .51 *** Cemetery .00 .02 -.02 *** Fitness .12 .21 -.09 *** RecPark .06 .09 -.03 *** N 36211 178115 ***- Significant at the .01 level

8

Table 4

Mean Price by Waterfront Property Type Variable N Mean StdDev Min Max

OnBay 196 1388218 963382.6 102600 6500000 OnCanal 1673 522737.2 514196.2 80000 5600000 • PremCanal 1572 540234.1 524635.3 82000 5600000 • NonPremCanal 101 250408.9 130221.7 80000 860000 OnRiver 636 338355.1 207818.3 25000 1395000 • PremRiver 212 389125.6 237623.5 75000 1395000 • NonPremRiver 424 312969.8 186360.3 25000 1100000

OnLake 2897 374965.2 319015.9 38000 3937500 • PremLake 868 534915.1 304506.3 44000 2300000 • NonPremLake 2029 306539.2 300090.8 38000 3937500 OnPond 30809 253988.6 161224.5 15000 4500000

Figure 1 Mean Price Movement Over Time by Waterfront Property Type

9

Table 5 Regression Model Output: Location and Distance from Bay

Dependent Variable: LnPrice

VARIABLE

MODEL 1A Coefficient

(Std. Error)**

MODEL 1B Coefficient

(Std. Error)**

MODEL 1C Coefficient

(Std. Error)** Constant 10.4091*

(.0435) 10.4238*

(.0438) 10.4695*

(.0416) Age -.0007

(.0006) -.0009 (.0006)

-.0020* (.0004)

Sqft .0004* (.0000)

.0004* (.0000)

.0004* (.0000)

Lotsize .0353* (.0078)

.0346* (.0077)

.0621* (.0083)

Bedrooms -.0329* (.0056)

-.0297* (.0055)

-.0235* (.0037)

Baths .0768* (.0069)

.0753* (.0066)

.0639* (.0049)

Stories -.0111 (.0131)

-.0157 (.0132)

-.0396* (.0091)

Pool .1279* (.0076)

.1234* (.0073)

.1121* (.0048)

Fireplace .0863* (.0120)

.0871* (.0120)

.0848* (.0097)

Garage .1748* (.0121)

.1715* (.0121)

.1978* (.0097)

ACSuperior .2569* (.0194)

.2538* (.0194)

.2258* (.0159)

ConstSuperior .1892* (.0276)

.1878* (.0279)

.1517* (.0205)

ExtWallSuperior .0600* (.0081)

.0583* (.0081)

.0549* (.0077)

FlooringSuperior .0926* (.0109)

.0913* (.0110)

.0735* (.0070)

RoofSuperior .1984* (.0225)

.1739* (.0194)

.1480* (.0185)

OnWater .0697* (.0122)

OnBay .7633* (.0532)

.7290* (.0589)

OnLake .1068* (.0354)

.1410* (.0319)

OnRiver .3839* (.0354)

.4796* (.0416)

OnCanal .4822* (.0343)

.4746* (.0353)

OnPond .0344* (.0100)

.0305* (.0087)

CBD_Distance -.0117* (.0023)

10

Bay_Distance .0012 (.0020)

GolfCourse_100yd .0488* (.0195)

MHParkExcellent -.0186* (.0041)

MHParkGood .0210* (.0040)

MHParkAvg .0291* (.0032)

MHPark<Avg .0084 (.0080)

RecPark -.0455 (.0214)

Cemetery -.0448 (.0397)

Fitness .0593* (.0192)

Y2001 .0792* (.0040)

.0793* (.0040)

.0846* (.0036)

Y2002 .1413* (.0054)

.1416* (.0054)

.1475* (.0051)

Y2003 .2264* (.0069)

.2276* (.0069)

.2370* (.0065)

Y2004 .3555* (.0080)

.3571* (.0081)

.3718* (.0074)

Y2005 .5597* (.0095)

.5621* (.0095)

.5812* (.0085)

Y2006 .6936* (.0111)

.6976* (.0112)

.7263* (.0089)

Y2007 .6214* (.0109)

.6253* (.0108)

.6535* (.0087)

Y2008 .4153* (.0091)

.4189* (.0091)

.4420* (.0077)

Y2009 .1395* (.0132)

.1423* (.0133)

.1703* (.0142)

Y2010 .1415* (.0109)

.1441* (.0110)

.1717* (.0117)

Y2011 .0706* (.0128)

.0735* (.0130)

.0996* (.0140)

Y2012 .0477* (.0151)

.0497* (.0152)

.0808* (.0165)

N 214326 214326 214236

R2 .7795 .7857 .8184

Bold- Significant at the 5% level; *- Significant at the 1% level **- Standard errors adjusted for clusters in STR

11

Table 6

Regression Model Output: Water Location and Time Dependent Variable: LnPrice

VARIABLE

MODEL 2A (Full Sample) Coefficient (Std. Error)**

MODEL 2B (Waterfront Only) Coefficient (Std. Error)**

Constant 10.4704* (.0417)

11.7109* (.1233)

Age -.0020* (.0004)

-.0027* (.0009)

Sqft .0004* (.0000)

.0004* (.0000)

Lotsize .0623* (.0083)

.0483* (.0070)

Bedrooms -.0234* (.0037)

-.0225* (.0054)

Baths .0636* (.0049)

.0444* (.0080)

Stories -.0395* (.0091)

-.0669* (.0082)

Pool .1122* (.0048)

.1343* (.0067)

Fireplace .0847* (.0097)

.0641* (.0097)

Garage .1981* (.0097)

.1439* (.0206)

ACSuperior .2261* (.0159)

.0607* (.0718)

ConstSuperior .1515* (.0204)

.1059* (.0210)

ExtWallSuperior .0552* (.0076)

.0308 (.0266)

FlooringSuperior .0735* (.0070)

.0374* (.0092)

RoofSuperior .1462* (.0187)

.1422* (.0241)

OnBay .7304* (.0587)

PremLake .2215* (.0903)

-.6080* (.1028)

NonPremLake .1072* (.0252)

-.7359* (.0603)

PremRiver .4809* (.0438)

-.3500* (.0673)

NonPremRiver .4793* (.0578)

-.4012* (.0753)

PremCanal .4764* (.0371)

-.3150* (.0496)

NonPremCanal .4579* (.0824)

-.3982* (.0963)

OnPond .0306* -.8209*

12

(.0088) (.0645) CBD_Distance -.0117*

(.0023) -.0100* (.0037)

BAY_Distance .0011 (.0020)

-.0001 (.0045)

GolfCourse_100yd .0493 (.0195)

.0485* (.0182)

MHParkExcellent -.0187* (.0041)

-.0043 (.0078)

MHParkGood .0210* (.0040)

.0014 (.0053)

MHParkAvg .0292* (.0032)

.0331* (.0030)

MHPark<Avg .0084 (.0080)

.0000 (.0097)

RecPark -.0456 (.0214)

-.0234 (.0324)

Cemetery -.0447 (.0396)

-.1227 (.0821)

Fitness .0593* (.0192)

-.0305 (.0170)

Y2001 .0846* (.0036)

.0767* (.0064)

Y2002 .1476* (.0051)

.1287* (.0088)

Y2003 .2370* (.0064)

.2245* (.0108)

Y2004 .3718* (.0074)

.3706* (.0141)

Y2005 .5810* (.0085)

.5886* (.0172)

Y2006 .7261* (.0089)

.7056* (.0168)

Y2007 .6535* (.0088)

.6264* (.0135)

Y2008 .4419* (.0077)

.4183* (.0132)

Y2009 .1701* (.0142)

.2449* (.0147)

Y2010 .1717* (.0117)

.2329* (.0151)

Y2011 .0997* (.0140)

.1843* (.0150)

Y2012 .0808* (.0165)

.2119* (.0153)

N 214236 36211 R2 .8186 .8460 Bold- Significant at the 1% level; *- Significant at the 5% level **- Standard errors adjusted for clusters in STR

13

Table 7 Regression Models 1A and 1C: By Year (Reporting Only Water Type Variables)

Dependent Variable: LNPRICE (**Standard errors adjusted for clusters in STR)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

COEF (SE)**

OnWater .0704* (.0097)

.0716* (.0125)

.0614* (.0126)

.0632* (.0125)

.0842* (.0162)

.0968* (.0180)

.0580* (.0175)

.0520* (.0138)

.0272 (.0149)

.0548* (.0158)

.0596* (.0160)

.0716* (.0158)

.1039* (.0189)

OnBay .4578 (.2018)

.6869* (.0728)

.6549* (.0786)

.6386* (.1057)

.7159* (.1275)

1.0169* (.0740)

.7576* (.0775)

.8424* (.0677)

.5918* (.1191)

.4495 (.3072)

.6050* (.1042)

.6813* (.0936)

.7788* (.1220)

OnLake .1672* (.0256)

.1578* (.0296)

.1331* (.0352)

.1339* (.0407)

.1407* (.0451)

.1920* (.0415)

.1183* (.0427)

.2025* (.0481)

.1510* (.0472)

.1295* (.0465)

.0918* (.0344)

.0787 (.0394)

.2115* (.0493)

OnRiver .3190* (.0597)

.3843* (.0456)

.4145* (.0537)

.4991* (.0658)

.5383* (.0469)

.5578* (.0480)

.3919* (.0818)

.5210* (.0687)

.5839* (.0841)

.4252 (.1719)

.5367* (.0949)

.5451* (.0739)

.5947* (.0706)

OnCanal .3697* (.0478)

.3941* (.0443)

.4387* (.0374)

.4506* (.0301)

.5723* (.0354)

.5903* (.0336)

.5113* (.0435)

.4455* (.0428)

.4798* (.0549)

.4550* (.0521)

.4014* (.0621)

.4199* (.0588)

.4449* (.0571)

OnPond .0344* (.0076)

.0291* (.0098)

.0191 (.0091)

.0254* (.0091)

.0380* (.0125)

.0481* (.0145)

.0347 (.0144)

.0301* (.0112)

.0097 (.0116)

.0212 (.0124)

.0224 (.0128)

.0252 (.0112)

.0297 (.0137)

N 18046 18736 19702 21808 23890 27239 19840 10631 9341 11000 10273 10916 12264 R2 .8215 .8271 .8208 .8367 .8346 .7968 .8367 .8393 .7998 .7698 .8012 .8058 .7928

14

Table 8 Price Premiums for Waterfront Properties Over the 2000s Decade*

OnWater OnBay OnCanal OnRiver OnLake OnPond Year PCT #Sales PCT #Sales PCT #Sales PCT #Sales PCT #Sales PCT #Sales 2000 7.29% 2823 58.06% 12 44.73% 180 37.58% 59 18.20% 194 3.50% 2378 2001 7.42% 2907 98.75% 15 48.30% 168 46.86% 69 17.09% 197 2.95% 2458 2002 6.34% 3067 92.50% 21 55.07% 173 51.36% 62 14.24% 233 1.93% 2578 2003 6.52% 3780 89.38% 11 56.93% 162 64.72% 65 14.33% 375 2.57% 3167 2004 8.79% 4043 104.60% 20 77.23% 165 71.31% 62 15.11% 375 3.87% 3421 2005 10.17% 4551 176.46% 17 80.45% 170 74.68% 72 21.17% 365 4.93% 3927 2006 5.97% 3154 113.32% 14 66.75% 62 47.98% 45 12.56% 248 3.53% 2785 2007 5.34% 1960 132.19% 12 56.13% 65 68.37% 25 22.45% 147 3.06% 1711 2008 2.76% 1801 80.72% 8 61.58% 70 79.30% 27 16.30% 133 0.97% 1563 2009 5.64% 2037 56.75% 10 57.62% 110 52.99% 30 13.83% 146 2.14% 1741 2010 6.14% 1958 83.13% 16 49.39% 101 71.04% 33 9.61% 152 2.27% 1656 2011 7.42% 2056 97.64% 19 52.18% 107 72.48% 36 8.19% 167 2.55% 1727 2012 10.95% 2074 117.89% 21 56.03% 140 81.25% 51 23.55% 165 3.01% 1697 Total 36,211 196 1,673 636 2,897 30809 *Converted from the regression coefficients in Table 7

15