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The Value of Wetlands for Flood Mitigation

Eric J. Ziemba,

Allison M. Borchers (USDA ERS)

Martin D. Heintzelman (Clarkson University)

Abstract

Floods cause billions of dollars in property damages each year in the United States.

While programs like flood insurance and disaster relief help to lessen the burden on

those harmed, they do nothing to mitigate the severity of floods. Wetlands have a natural

ability to mitigate the impacts of flooding through the slowing of runoff and absorption of

excess rainwater. For this purpose they are inherently valuable. This study implements a

hedonic analysis to study the complicated interactions between property values,

wetlands, and flood risk using detailed property transaction data in the Hudson River

Valley of New York State. We augment this analysis with GIS data including FEMA flood

risk maps, the location of wetlands and other land uses, and various geographic layers.

We find, as expected, that higher levels of flood risk, as well as the realization of actual

flooding events, negatively impact property values. We also find that the presence of

wetlands mitigates the negative effect of floods to properties in flood zones, which is

likely due to a reduced severity of damages. The magnitudes of these effects suggest that

wetlands provide considerable benefits to property owners and the results provide a

partial benefit measure for policymakers, especially to state and federal programs, such

as the USDA’s wetlands and conservation reserve programs whose primary aim is to

preserve and restore these natural buffers.

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Floods are the most common natural disaster according to the National Flood Insurance

Program (NFIP). They cause extensive damages, 2.9 billion dollars annually on average,

to properties within flood plains in the United States. Indeed, evidence suggests that past

flood events, as well as the threat of future floods may harm property values (Bin and

Polasky, 2004; Bin et al., 2008; Harrison et al., 2001; Samarasinghe and Sharp, 2010;

Turnbull et al., 2013). One way that property owners realize flood threat is through areas

identified as high-risk. These high-risk areas, called flood plains, are published in Federal

Emergency Management Agency (FEMA) flood maps, and categorized by the percent

risk of a flood each year. Flood maps are used in real estate and mortgage markets

making them a common, well-understood measure of flood risk for property buyers.

Although flood insurance is available from the federal government at a subsidized rate to

households within FEMA designated high-risk areas this does not eliminate the threat of

floods. A recent discussion piece from Resources for the Future addresses the potential of

using floodplain conservation in general as a method for mitigating flooding (Kousky and

Walls, 2013). We hypothesize that the presence of wetlands will play a significant role in

mitigating the negative impact of flooding on property values by reducing the severity of

damages.

Wetlands are a vital piece of riparian, forest, and coastal ecosystems.1 They provide

valuable habitats for many species, including fish, frogs, turtles, and numerous varieties

1The U.S. Fish and Wildlife Service’s Classification of Wetlands and Deepwater Habitats of the United

States, defines wetlands as follows; “WETLANDS are lands transitional between terrestrial and aquatic

systems where the water table is usually at or near the surface or the land is covered by shallow water. For

2

of migratory and non-migratory birds. Wetlands also provide a number of different

services to human populations. Being home to fish and waterfowl, they are popular

locations for recreational fishing and bird hunting, as well as other outdoor recreation

activities such as kayaking, bird watching, and hiking (Zedler and Leach, 1998).

Arguably one of the most important services they provide is their ability to mitigate

flooding (Bullock and Acreman, 1999). The soil composition acts like a sponge to soak

up excess runoff, reducing the amount of water that ends up in rivers. Soil also plays a

role in filtering toxins out of the surface and ground water. All of these “ecosystem

services” benefit communities, whether visibly or in the background. The value of these

services is not generally captured in existing markets (Costanza et al., 1997), which

means that they can be thought of as a positive externality or public good, which

generally implies that they will be underprovided. Therefore, non-market valuation

methods must be used for the valuation of these services.

We aim to partially quantify the benefit of wetlands for flood mitigation using hedonic

analysis. This approach assumes the market value of a good (often residential properties)

is related to the characteristics or the services the good provides. Hedonic analysis is well

documented in the literature (Bastian et al., 2002; Parmeter and Pope, 2009; Rosen, 1974;

Taylor, 2003) and has been used extensively to value many environmental amenities, air

purposes of this classification wetlands must have one or more of the following three attributes: (1) at least

periodically, the land supports predominantly hydrophytes;' (2) the substrate is predominantly undrained

hydric soil;2 and (3) the substrate is non soil and is saturated with water or covered by shallow water at

some time during the growing season of each year.”(Cowardin, 1992, p. 3)

3

quality (Smith and Huang, 1995), the proximity to landscapes and open space (Cavailhès

et al., 2009; Kong et al., 2007), or the population of important or endangered species

(Loomis and White, 1996) as well as dis-amenities, such as hog farms (Palmquist et al,

1997). Many hedonic analyses of wetlands prior to this study were focused on

environmental amenities, mainly the recreational opportunities available to communities

(Brander et al., 2006). For other studies that examined ecosystem services, such as flood

protection and other hydrologic services, replacement costs and the production function

have been more common methods of valuation (Acharya and Barbier, 2000; Acharya,

2000; Gren, 1995).

Most existing studies of wetland valuation focus on small areas, often showing the value

of one wetland for conservation purposes (Fan and Yang, 2010; Frey et al., 2013).

Therefore, the value of wetlands in much of the previous research has been focused on

specific wetlands that are known to exist by homebuyers (Bin, 2005; Mahan, 1997).

However, wetlands may provide value in housing markets even if homeowners do not

have information about the existence and/or characteristics of nearby wetlands. This is

because homes with more nearby wetlands may be less likely to be damaged by floods,

and this reduced damage may be noticed and capitalized into property values.

This article is focused on seven counties within the Hudson River valley region in New

York State, generally a larger geographic area than past studies of this nature, allowing

for a more general depiction of the value of flood mitigation provided by wetlands to

landowners. We use detailed residential and agricultural property transaction data over

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the period 2004-2012, including information on the structures (for example, houses and

barns) as well as on the parcel itself. This data is augmented with a rich set of geospatial

data, including Digital Flood Insurance Rate Maps (DFIRMs) from FEMA, the location

of wetlands and other land uses, and various topographical and other geographic layers.

Given the impacts of flooding and the benefits provided by wetlands, it is likely that their

presence or absence interacts with real and perceived flood risk to impact property

values. That is, wetlands are likely to have more value to homes facing higher flood risks,

ceteris paribus. We capture these effects using FEMA flood risk to categorize properties

into their corresponding flood zones.

In addition, actual flooding events may also have short-term impacts on property values,

and affect the way wetlands and flood risk are capitalized. To capture this effect, we use

data on four flooding events that occurred in the Hudson River Valley during our study

period. It is expected that higher levels of flood risk, as well as the realization of actual

flooding events, will negatively impact property values, but that the presence of wetlands

will mitigate that effect.

Utilizing a triple interaction term (between a parcel’s stated flood risk, the occurrence of

major flooding, and the presence of wetlands), our results find significant capitalized

property values for wetland flood mitigation benefits following major flooding events.

This work, from a policy perspective, further supports public spending on wetland

restoration and mitigation efforts. Like many areas of the country, FEMA has recently

announced an expansion of the 100-year flood plains throughout New York State, to

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include 35,000 more properties than the current maps (Buckley, 2013). The results found

here may help policy makers understand the impacts of both flooding and wetlands on an

increasingly high number of property owners.

Existing research on flooding and wetland valuation using Hedonic Analysis

A fairly recent trend has been to utilize hedonic analysis in the study of environmental

amenities and dis-amenities. By including environmental variables like air quality (Smith

and Huang, 1995), the proximity to certain geographic features (Cavailhès et al., 2009;

Kong et al., 2007), or the population of important or endangered species (Loomis and

White, 1996) into econometric regressions, the value that those amenities add to the price

of a home can be understood. In order to make this type of research more effective,

Geographical Information System (GIS) software is frequently utilized to conduct spatial

analysis to be used with hedonic analysis.

The effect of flooding on property values has been studied rather extensively in the

hedonic literature. Most papers utilize currently available FEMA flood data to conduct

their analysis (Harrison et al., 2001; MacDonald et al., 1990; Speyrer and Ragas, 1991),

as we do. The studies have shown that living in a high risk, 100-year, flood zone

significantly reduces property values in comparison to similar homes with little to no

flood risk. In addition, flooding events have a similar effect which has been shown to be

diminishing over time after an event. Recent papers suggest that this discount for flood

zone properties after a significant flooding event decays quickly, disappearing completely

in as little as four to five years (Atreya et al., 2012; Bin and Landry, 2012).

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Although not all floods are caused by hurricanes and other news worthy storms, these

events capture the public imagination and are likely to increase the salience of flooding

and risk in real estate markets. Bin and Polasky (2004) study property values in and out

of flood zones following Hurricane Floyd in North Carolina. Their dataset includes

transaction from 1992-2002, which allows them to observe the effect of the storm for

three years after the fact. They find a significant decline in property values after the event

when their post-flood variable is interacted with the property being within a high-risk

flood zone. We utilize a similar approach of interacting floods with homes in flood zones,

focusing on the short-term effects, with additional focus put on the role that wetlands

play in flood mitigation and the utilization of year fixed effects.

Much of the previous research examining the value of wetlands through hedonic analysis

has been focused on small and localized areas, such as agricultural properties in a region

of Wyoming (Bastian et al., 2002), a multiuse urban wetland in Southern California (Frey

et al., 2013), or neighboring towns in Connecticut (Paterson and Boyle, 2002). While

flood mitigation is the primary focus of this article, wetlands may also provide the

community with recreational amenities, a rich and bio-diverse ecological community,

open-space, carbon sequestration (especially considering forested wetlands), and cleaner

water. Open-space is a general natural amenity, not only associated with wetlands, whose

value has been studied in great detail using hedonic analysis (Sander and Polasky, 2009;

Smith et al., 2002). Open-space can be valuable for specific amenities especially in urban

areas, or simply as a lack of development. In addition, open-space can be viewed as an

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amenity that might span across communities, much like the effects of wetlands, so it

seems reasonable to assume that hedonics might be a worthwhile tool in determining

their value. Okmyung Bin used hedonic analysis to study wetlands for a relatively small

community in Northern Portland (2005), and acknowledges the methods effectiveness,

though his study was limited to single family residential properties. Expanding a similar

study to include all residential properties and agricultural properties may lead to a greater

understanding of the effect of wetlands on property values.

A recent paper looks at the effect of restored, as well as natural, wetlands. While they

find that wetlands play a significant role in raising property values, it seems that the

restored type may harm properties nearby (Kaza and BenDor, 2013). This effect reverses

in properties that are greater than half a mile away, suggesting that the reduction in

property values may be linked to either the construction process, or a poor reputation of

the region’s ecosystem enhancement program. When the restoration process is less

observable, their results are similar to Ghermandi et al. (2010), which finds significantly

positive effects of property values, as part of a meta-analysis. This positive effect is

attributed highly to the improvements in water quality, biodiversity, and recreation,

provided by the new wetlands.

This research adds two important aspects to the literature. First, it broadens the

geographic scope of wetland valuation by observing the effect of wetlands, in relation to

flood zones, over a five county region. Second, we utilize a triple interaction term to

quantify the ecosystem of flood mitigation. Through the use of our triple interaction term

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we are able to show that the presence of wetlands significantly reduces the impact that

flooding has on property values in high-risk areas. Higher property values in this

scenario, in relation to homes without wetlands nearby, is likely do to a reduction in the

damages caused by flooding thanks to the flood control services, which are known to be

provided by wetlands.

Methodology

The price of a home is determined by many factors; the size, features, condition, location,

and nearby amenities to name a few. The hedonic price model is utilized to calculate the

utility of the individual characteristics that make up that price. Although hedonic analysis

existed before Sherwin Rosen’s Hedonic Price and Implicit Markets: Product

Differentiation in Pure Competition (1974), this article is considered to be a seminal

piece in the field. Rosen’s publication developed an econometric framework that made

connections between the utility of individual characteristics for heterogeneous goods and

the equilibrium price. This provided evidence to say that specific characteristics raise or

lower the value of a good, ceterus paribus. Hedonic analysis has been used to study many

different things beyond the value of the individual characteristics of a home. Some

interesting examples include; open space in urban areas (Smith et al., 2002), the effects of

repeated wildfires (Mueller et al., 2009), riparian buffers (Bin et al., 2009), crime and

education (Dubin and Goodman 1982), railway and other public transportations (Mayor

et al., 2012), historic preservation (Heintzelman and Altieri, 2011), the premium paid by

customers for organic cotton (Nimon and Beghin, 1999) or baby food (Maguire et al.,

2004), and prescription drugs for arthritis (Cockburn and Anis, 2001).

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Our analysis utilizes the hedonic price model, which, like any empirical approach, is

subject to certain econometric challenges, many of which can be addressed using a fixed

effects approach (Greenstone and Gayer, 2009; Kuminoff et al., 2010). Fixed effects

estimation allows for multiple intercepts at the designated level of the fixed effects:

census block, block-group, tract, etc. This helps to control for otherwise omitted factors,

which are constant at the level of the fixed effects, but vary spatially between different

areas. The estimation of other effects then relies on variation within fixed effects zones.

In our case, we are hoping to identify how variation in flood risk and wetlands, within

small geographic areas (usually census blocks) following a flood, impacts relative

property values. While the magnitude of wetlands is likely to be very similar between

homes in the same census block, they may interact differently with homes depending on

their flood zone status.

We use log-log and log-linear functional forms, as is standard in many hedonic analyses.

Heteroskedasticity is dealt with by clustering the error terms in our regressions. In

standard OLS regressions concerning property values, the error terms of each house are

assumed to be independent of one another. In real life this is untrue since homes nearby

to each other are likely to be similarly affected by omitted variables. Some of this is

controlled for through the use of local area fixed effects, especially when using census

blocks; however there is still concern over the assumption that error terms are

independent. Clustering the error terms relaxes this assumption and allows for correlation

within the block, but continues to assume independence of error terms between blocks.

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In addition, we normalize sales prices to 2000 dollars using the quarterly House Price

Index (HPI) from the Federal Housing Finance Agency. Properties within a Metropolitan

Statistical Areas (MSA’s) utilized the corresponding HPI; all others were adjusted using

the general state level HPI.

Data

Our data is comprised of a nearly complete set of 50,135 property transactions throughout

five counties in the Hudson Valley region of New York State. These parcels consist

primarily of residential properties, though we also include agricultural parcels that are

categorized as including a permanent residence. The counties (Dutchess, Greene, Orange,

Putnam, and Ulster) were chosen for a number of reasons. This region has been subject to

a number of serious flooding events in the past decade with notable storms including

Tropical Storm Irene and Hurricane Sandy, although Sandy is not included in our study

since it happened so late in 2012. A table of summary statistics can be seen in the

appendix, and includes information on average sale price, property characteristics, and

the proximity to relevant environmental features.

All of the data on parcel level transactions were collected from the New York State

Department of Taxation and Finance (NYSDTF) SalesWeb application. Parcel level

characteristics were added to the data from the NYS Office of Real Property Tax Services

(ORPTS). The data collected here include information on the number of bedrooms,

bathrooms, fireplaces, etc., as well as square footage, building condition and other useful

variables, which are essential to understanding building and land valuation. This data was

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downloaded for every property known to the Department of Taxation and Finance, so we

merged it with the transactions data, dropping out any parcels that did not transact within

the years of our study.

To be able to take advantage of the spatial analysis capabilities of GIS, it was necessary

to collect polygon data for all parcels within the region of study. These were collected

from either the New York State GIS (NYSGIS) Clearinghouse or individual county

offices. The parcel polygons, once in a consistent format, were merged together to create

a single GIS layer for the entire set of counties. Data on the geographic location of

community and environmental features were also downloaded primarily from the

NYSGIS Clearinghouse. Flood zone data was accessed through the FEMA Map Service

Center, msc.fema.gov. These maps, generally in a GIS format, give an account of each of

the flood zone designations in a given area. These flood inundation maps, produced by

FEMA, are associated with a 100-year Base Flood Elevation (BFE) in an attempt to

measure the likelihood that a property will have a severe amount of flooding each year

(Sperl, 2013). Additional hydrologic analyses may be employed by outside agencies to

ensure the relevancy of the BFE in risk assessments. Although wetlands play a role in

controlling flow rate there is little to no mention of them in NFIP documentation

concerning the designation and regulation of flood zones. This is important because if

wetlands play a direct role in determining the flood zone classification of a property than

our measures of flood risk and wetlands will be positively correlated which will bias our

estimates of the effects of each towards zero. This data is frequently used in research

regarding flood hazards, and plays a critical role in this analysis.

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The limited scope of the FEMA flood zones makes understanding the real risk of

flooding, on a parcel-by-parcel basis, somewhat difficult. These flood zones represent a

generalized, discrete, risk of flood damage, rather than a more precise, parcel-specific,

measure. However, we choose to rely on this approach because of the way NFIP data is

used in real estate and mortgage/insurance markets, making it a common, well-

understood measure of flood risk for homeowners.

Polygons for the transacted parcels were mapped using GIS (Environmental Systems

Research Institute, 2012) to allow the analysis of community level and geographic

features that were of interest. For a consistent distance measurement we created centroids

(central points) for each parcel. These were then spatially joined to a number of important

features such as, towns, major cities, streams, ponds, and highways. Doing this provided

distance measurements (in meters) to the nearest feature from each parcel.

Three categories of wetlands are included in the data we use from the U.S. Fish and

Wildlife Service (USFWS) National Wetlands Inventory: Freshwater Forest/Shrub

Wetland, Freshwater Emergent, and Estuarine and Marine Wetland (Cowardin, 1992). In

our analysis we choose to aggregate these wetland types into a single classification. It is

our understanding, after discussion with experts and a review of literature, that the ability

of wetlands to mitigate floods is determined primarily by their location and quality

(Bullock and Acreman, 1999; Zedler and Kercher, 2005; Zedler and Leach, 1998) as

opposed to their classification. Since the focus of our study is on flood mitigation rather

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than other attributes of wetlands, the aggregation of those types into one category seems a

reasonable course of action. While understanding that distinct types of wetlands exist and

play similar roles in this specific ecosystem service is important, the cumulative impact

of all nearby wetlands on flood mitigation is the important factor in this research.

Wetlands were initially integrated into the analysis using the same distance

measurements utilized for other features. However, due to the large number of small

wetlands in the area, we postulated that understanding the total acreage of wetlands

nearby would be more relevant. To determine the total acreage of wetlands in the vicinity

of each parcel, buffers were created around each centroid at distances of two and five

Kilometers. The sizes of the buffers were based on a desire to understand the effects of

nearby wetlands, and stops at five kilometers because of computational limitations. Those

buffers were then spatially joined to the wetland polygons, providing the number of

wetlands that fell at least partially within the buffer, and the total acreage of them. The

geographic features, produced in GIS, were integrated with the parcel level transactions

data using STATA (Stata Corp., 2011).

By interacting flood zone and wetland acreage variables with dummy variables flagging

properties sold within three or six months of a major flooding event, we examine the

average effect that wetlands have on property values in flood zones when perceived risk

is at an elevated state. The events were chosen by referencing a list of major flooding

events from the National Weather Service (NWS) (US Department of Commerce, n.d.)

and finding floods in the lower Hudson Valley region during the study. Four floods were

found to be important. They included flooding that occurred throughout April of 2005 in

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Ulster County, flooding through October of 2005 in Dutchess and Greene Counties, and

more specific events on April 15-16 in 2007 that hit the entire study area, and finally

Tropical Storm Irene in late August of 2011. The dummy variables for these “flood

windows” were constructed in such a way that they are only activated in counties that

were hit by flooding.

In our regressions, the total acreage of wetlands near to the parcel is interacted with a

dummy variable for being located within a 100-year flood zone. This interaction term

demonstrates the effect that wetlands have on properties purchased in a 100-year flood

zone. Since flood zones and flooding events likely reduce property values, and wetlands

can reduce the severity of floods, we would expect to find that having wetlands in the

vicinity would reduce the negative effects of living in a high-risk flood zone. Figure 2

illustrates the relationship between flood zones and the wetlands in the counties we study.

An elevation of perceived flood risk, for instance after a major storm, may have an

adverse effect on a consumers willingness to pay for a home (Kousky, 2010), so we take

this interaction term a step farther than is typical in this area of research. The triple

interaction term includes the total acreage of wetlands, the flood zone dummy variable,

and a dummy variable for transactions, which occurred within either the three or six

months post-flood window.

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Figure 1. Study region location within state of New York with Wetland and Flood Zone

Locations

Results

The results of our analyses are reported below for the Census Block fixed effects level.

We also ran regressions at the block-group level, a larger geographic area, as a robustness

check and we briefly discuss these results below. Table 1 presents the results of the

Census Block fixed effects for each of three specifications using different flood windows.

For brevity we report only the variables of primary interest to this analysis2. However, the

models have good fit and many of the standard structural and parcel characteristics have

the expected sign and significance. For example, the number of bathrooms and fireplaces,

2 Complete results are available upon request.

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along with the total acreage and square footage of the parcel show positive and

significant coefficients. The overall condition of the home, and whether it has central air

also produced positive and significant results, while the number of kitchens (indicating

that the unit may be, or was recently, a multi-family unit) and the age of the home were

negative and significant. Proximity to ponds, the Hudson River, and NYC increase sale

price, but distance from streets and town centers is preferred. Having a stream or pond

intersecting the property increases property values, as does the elevation of the parcel. All

of these results are consistent with literature utilizing the hedonic pricing model.

As expected, we find that the dummy variable for a parcel being in a 100-year flood zone

was significant and negative—between -.073 and -.083. This translates to a reduction in

property values between 7.9-8.6% on average (following the method reported in

Halvorsen and Palmquist (1980)). This result is only slightly higher than Bin and

Polasky (2004), who report a 5.7% reduction.

We find that both the three-month and six-month flood windows were insignificant in

effecting property values for the entire region, which is not surprising since homes not

affected by the floods (outside of flood zones) are unlikely to experience any significant

depreciation as a result of the flooding events. When interacting these variables with

properties in 100-year flood zones, however, we observe coefficients that are negative

and significant showing that major floods harm property values in flood prone areas more

than the region as a whole. This interaction term suggests that property sales within the

100-year flood zone and within three months of a major flooding event experience a

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30.9% reduction in value relative to homes that are not located in flood zones and sold

prior to flood events. While the effect dissipates slightly for properties sold within six

months, those are still being reduced as much as 19.1%. Notice that we are using all four

storm events simultaneously in these specifications, so these estimates represent the

average flood effect across these events. These results support the findings by Bin and

Polasky (2004), providing further evidence that property values in 100-year flood zones

are harmed more severely by a major flooding event than are areas of lower risk.

No evidence that wetlands themselves have an effect on nearby property values is found,

nor do they have an effect within flood zones, as variables that interact acres of wetlands

within various buffers with the flood zone dummy variables are not significant.

However, we hypothesized that the value of wetlands might be most relevant in the wake

of flooding.

Using the interaction terms between flood zones and a transaction occurring three or six

months after a severe flood we then bring in the total acreage within each of the buffers

by creating a new interaction term. Through the use of this triple interaction term we are

able to see how the presence of wetlands within each buffer affects the price of homes in

flood zones that sold shortly after a major flood. The coefficients for these variables give

more significant results as the size of the buffer increases suggesting that the total acreage

of wetlands must be at a high magnitude, with less regard to the proximity, in order to be

significant. We find that when these interaction terms utilize either the two-kilometer or

five-kilometer buffers the results are positive and significant. This indicates that, while

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flood zones and major floods reduce the value of a home, ceterus paribus, the presence of

a large volume of wetlands helps to mitigate that negative effect. In essence, homebuyers

purchasing after a flood are willing to spend more on a home in a flood prone area when

a significant system of wetlands is in place. The presence of wetlands within the larger

buffers show no significant results on their own or when interacted directly with

properties in flood zones. However, since they do show significant results in this specific

situation, it is reasonable to attribute that effect to the flood mitigation service provided,

and not to other ecosystem services or recreational amenities that they may provide. It is

unlikely that the buyer observes all wetlands within five kilometers of a home, although

the reduced effect of floods due to their presence could likely be observed in the form of

decreased damages in comparison to areas that have fewer wetlands.

For properties which sold within the three month flood window which are within the

flood zone we find a positive coefficient of 0.000247 with increasing wetland acreage

within both the two and five kilometer buffer. Given that the average property has 198

acres of wetlands within 2k and 1115 acres within 5k, these results represent a potential

increase in property values of around 27.5% when an average amount of wetlands are

nearby homes that are in flood zones and sell after a flood occurs relative to properties

with no wetlands within these buffers. In the regressions that focused on transactions

within six months of a major flood, again, positive and significant results for wetlands in

both the two and five kilometer buffers are seen. The estimated effect is slightly less than

what we observed in the three-month window, with a slightly reduced level of

significance. If an average number of wetlands within five kilometers exist around a

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parcel, our results suggest that property values after a flood could be 14% higher, on

average, than parcels that do not have wetlands nearby.

Table 1. Wetland and Flood Zone Results for Specified Flood Windows and Buffer Sizes

To illustrate the value of a useful scenario, note the average total acreage within a 5km

buffer is 1,154. With a mean sale price of $306,435 a major flood is expected reduce the

value of properties within a flood zone, with no wetlands within 5km, by an estimated

$79,422. Within our sampled properties the average acreage of wetlands within a 5km

buffer is 1,154. Given a nearly average volume of wetlands within a five-kilometer radius

(1,000 acres) it is estimated that property values could only be discounted by $32,742—a

difference of over $46k. This mitigation in the loss of value following a major storm is

likely due to a reduction in observed damages. Given the role of wetlands as flood buffers

we attribute this significant mitigation to the presence of wetlands in the area.

Flood Window

2K Buffer 5K Buffer 2K Buffer 5K Buffer 5K Buffer

Variables

100yr Flood Zone -0.083*** -0.082*** -0.083** -0.083*** -0.073**

500yr Flood Zone -0.031 -0.031 -0.031 -0.031 -0.031

Wetland Acreage (within buffer) -0.000 0.000 -0.000 -0.000 0.000

Wetland Acreage * 100yr FZ 0.000 0.000 0.000 0.000 0.000

Property Sold in Flood Window -0.005 -0.005 -0.010 -0.010 -0.008

Sold in Window * 100yr FZ -0.226** -0.226*** -0.108** -0.108** -0.042

Sold * FZ * Acreage of Wetlands 0.000247*** 0.000247*** 0.000145*** 0.000145*** 0.000

note: *** p<0.01, ** p<0.05, * p<0.1

3-6 Months 0-6 Months 0-3 Months

Coefficient

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Interestingly, we can also observe how the effect of flood zone dissipates over time

following a flood event. We find that the additional negative effects of a flood zone in the

0-3 month window following a major storm dissipates in the following 3-6 month period.

Since the effect of the flood zone dissipates during the second period, it is not surprising

that the triple interaction term becomes insignificant as well. This is likely because that

service from wetlands is no longer being observed. Additional research should be

conducted to investigate the duration of flood mitigation services to the housing market.

Specific major flooding dates were also regressed individually using dummy variables for

sale before or after those floods. Throughout the regressions for the individual flood

events, we do not find consistently significant results for the flood zone interaction terms

or for the three-way interaction term between flood zones, flood dates, and the total

acreage of wetlands in the area. Although insignificant, the coefficients from these are

positive, which is promising. It suggests that it is a lack of observations leading to

insignificance rather than that there is no effect.

Results of block-group fixed effect model

Many of the flood and wetland related variables were of the same sign in our block-group

level regressions when compared to the smaller census block, although they were in

general less significant or insignificant. Flood zones and the flood date variables, both

individually and together were still negative and significant. We also see a positive and

significant result from the two and five kilometer wetland buffer interacted with the flood

zone/flood date variable for sales within six months of a flood. On this larger census level

21

we also see more significant results for housing characteristics and geographic variables.

Notably, we can now see that within a block-group the distance to a lake becomes

significant showing that homebuyers do prefer to be near to a lake. This likely did not

show up in the block level regressions because of the distance to the nearest lake within

homes in the same block is so homogenous.3

Conclusions

The purpose of this research was to investigate the possible effects that wetlands have on

reducing the negative effects on properties within a flood prone area. Consistent with

previous literature our results find that flood zones negatively impact the value of those

properties within the flood zone. While we do not find a direct connection between

wetlands and property values we do find that in the wake of severe flooding, wetlands

play a significant role in mitigating the harm done to values of properties within the 100-

year flood zones. This indicates that the value of wetlands in flood prone areas may be

tied more closely to the flood mitigation service they provide than to other services or

amenities, or that the flood mitigation provided by wetlands reduces the visible damages

caused by flooding that would have otherwise lowered property values. Logically, we

find the value of that service increases as the total acreage of nearby wetlands increases.

3 At the block-group fixed effects level we are also able to capture a trend of increasing home prices as the

location moves from Albany to NYC, and buyers prefer to be near to the highway I-90. We do not find

significant results for I-87, also called “The Northway”, which is probably due to its proximity to the

Hudson River which shows very significant results suggesting that people prefer to be closer to it, just as

we saw in the block level regressions.

22

In addition, these results suggest that property owners are willing to pay more for

properties with nearby wetlands regardless of their visibility. This departs from past

literature, which suggests that visible wetlands increase property values (Boyer and

Polasky, 2004). Therefore our results suggest that not only are wetlands an important

feature for the ecology of the regions and the riparian systems, they provide an ecosystem

service that is valued in the real estate market beyond their visual or amenity

characteristics.

As flooding becomes a more frequent event, and flood maps are revised or redrawn

(Buckley, 2013), there is an opportunity for efficient investment into the conservation and

restoration of wetlands. Wetlands are a natural way to reduce the physical risk of

flooding, and have the potential to be far more visually pleasing and offer a greater

diversity of services than other options, such as a concrete dyke. Wetlands’ flood

mitigation could reduce the costs associated with flooding to property owners, as shown

in this article, but also to the NFIP. Through investments in wetlands and floodplain

conservation the NFIP might reduce its own financial risks. With less damaging flooding

events the rate and amount of claims needing to be paid to policyholders could be

reduced, and may also aid in generating a larger reserve fund for times of true disaster

when large claims may still need to be filed.

In addition, the evidence presented in this article directly provides Federal, State and non-

government conservation organizations, such as New York State’s Department of

Environmental Conservation or Federal programs such as the Conservation Reserve

23

Program’s wetland restoration initiative or the Agricultural Conservation Easement

Program’s Wetland Reserve Easements (formerly the Wetland Reserve Program), an

understanding of the value that wetlands add to communities. Specifically, estimating the

value added to properties by the flood mitigation service of wetlands provides a benefit

measure to these Federal, State or non-government conservation organizations when

wetlands are threatened by new development projects or expanding farmland acres.

Furthermore, when wetlands must be filled in to make room for new development, and

wetland mitigation banking is used, the location of new wetlands is a heated topic. The

significant value demonstrated in this article for wetlands within five kilometers of homes

in flood zones suggests significant value of constructing new wetlands very near to the

originals.

Estimating benefit measures of wetland services is an important step in supporting efforts

to protect and restore wetlands for the valuable services that they provide the community

by reducing both the severity of floods and bolstering property values in the wake of

major flooding events. Though this research suggests significant value for wetlands based

on their mitigation of damages caused by floods, it is not definitive. More research, in

other hurricane and flood prone regions, is necessary to develop a greater understanding

about the benefits that wetlands provide to housing markets and the community.

24

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30

APPENDIX:

Demographics

United States

New York State

Dutchess County

Greene County

Orange County

Putnam County

Ulster County

$59,511

$85,570

$108,241

$71,743

19,378,104.00

308,745,538.00$50,054

$50,636

$84,881

Median value of owner-occupied housing units,

2007-2011

Total Population 2010 (April,1) estimates base

2011 Mean Income (2011 Dollars)

$242,500

$406,000

$299,500

$181,300

$314,500

$301,000

$186,200

182,494.00

99,724.00

372,813.00

49,221.00

297,474.00

31

Summary Statistics

32

Block Level Results – 3-Month Flood Window

Variables coef se coef se

Property in 100-year Flood Zone -0.083*** 0.029 -0.082*** 0.026

Property in 500-year Flood Zone -0.031 0.023 -0.031 0.024

Acreage of Wetlands within 2 Kilometers -0.000 0.000

Acreage of Wetlands within 5 Kilometers 0.000 0.000

Acreage of Wetlands(2k) * 100yr Flood Zone 0.000 0.000

Acreage of Wetlands(5k) * 100yr Flood Zone 0.000 0.000

Flood Occurred 0-3 Months Prior to Sale -0.005 0.015 -0.005 0.015

Flood (0-3 Months) * 100yr Flood Zone -0.226** 0.089 -0.226*** 0.079

Flood (0-3 Months) * Flood Zone * Wetlands(2k) 0.000*** 0.000

Flood (0-3 Months) * Flood Zone * Wetlands(5k) 0.000*** 0.000

Acres 0.003* 0.001 0.003* 0.001

Number of Kitchens -0.078*** 0.016 -0.078*** 0.016

Number of Full Bathrooms 0.097*** 0.025 0.097*** 0.025

Number of Half Bathrooms 0.040** 0.020 0.040** 0.020

Number of Bedrooms 0.012 0.012 0.012 0.012

Number of Fireplaces 0.079*** 0.011 0.079*** 0.011

Central Air 0.048** 0.019 0.048** 0.019

Overall Condition 0.102*** 0.026 0.102*** 0.026

Square Footage 0.000*** 0.000 0.000*** 0.000

Age -0.001** 0.000 -0.001** 0.000

Age2 0.000 0.000 0.000 0.000

Agriculture -0.404 0.436 -0.404 0.436

Two Family -0.094*** 0.034 -0.094*** 0.034

Three Family -0.165** 0.066 -0.165** 0.066

Rural with Acreage 0.351*** 0.044 0.351*** 0.044

Estate 1.381*** 0.385 1.381*** 0.385

Seasonal -0.816*** 0.188 -0.816*** 0.188

Mobile Home -0.742*** 0.214 -0.741*** 0.214

Multi Residences 0.305*** 0.048 0.305*** 0.047

ln Dist. to Nearest Streams 0.003 0.006 0.003 0.006

ln Dist. to Nearest Lake 0.006 0.009 0.006 0.009

ln Dist. to Nearest Pond -0.008* 0.005 -0.008* 0.005

ln Dist. to Nearest DEC Land 0.002 0.012 0.002 0.012

ln Dist. to Nearest DEC Trail 0.001 0.005 0.001 0.005

ln Dist. to Nearest Towncenter 0.016** 0.007 0.016** 0.007

ln Dist. to NYC -0.155** 0.063 -0.155** 0.063

ln Dist. to Albany -0.053 0.107 -0.053 0.103

ln Dist. to Nearest Street (35-45 mph) 0.014*** 0.005 0.014*** 0.005

ln Dist. to Interstate - 90 -0.055 0.084 -0.055 0.080

ln Dist. to Interstate - 87 -0.006 0.008 -0.006 0.008

ln Dist. to Nearest Railroad 0.017 0.011 0.017 0.011

ln Dist. to Hudson River -0.027** 0.013 -0.028** 0.013

ln Dist. to Nearest Bridge over Hudson River 0.009 0.010 0.009 0.010

Acreage of nearest DEC Lands -0.000 0.000 -0.000 0.000

Acreage of nearest Lake 0.000* 0.000 0.000* 0.000

Acreage of nearest Pond -0.001 0.001 -0.001 0.001

Stream Intersects Property 0.073*** 0.009 0.073*** 0.009

Pond Intercepts Property 0.087*** 0.022 0.087*** 0.022

Lake Intercepts Property 0.147 0.100 0.147 0.100

Elevation in Meters 0.001*** 0.000 0.001*** 0.000

Year and Month Dummies Yes Yes Yes Yes

_cons 15.190*** 1.483 15.193*** 1.470

note: *** p<0.01, ** p<0.05, * p<0.1

2K Buffer 5K Buffer

33

Block Level Results – 6-Month Flood Window

Variables coef se coef se

Property in 100-year Flood Zone -0.083** 0.033 -0.083*** 0.030

Property in 500-year Flood Zone -0.031 0.023 -0.031 0.024

Acreage of Wetlands within 2 Kilometers -0.000 0.000

Acreage of Wetlands within 5 Kilometers -0.000 0.000

Acreage of Wetlands(2k) * 100yr Flood Zone 0.000 0.000

Acreage of Wetlands(5k) * 100yr Flood Zone 0.000 0.000

Flood Occurred 0-6 Months Prior to Sale -0.010 0.011 -0.010 0.011

Flood (0-6 Months) * 100yr Flood Zone -0.108** 0.052 -0.108** 0.052

Flood (0-6 Months) * Flood Zone * Wetlands(2k) 0.000*** 0.000

Flood (0-6 Months) * Flood Zone * Wetlands(5k) 0.000*** 0.000

Acres 0.003* 0.001 0.003* 0.001

Number of Kitchens -0.078*** 0.016 -0.078*** 0.016

Number of Full Bathrooms 0.097*** 0.025 0.097*** 0.025

Number of Half Bathrooms 0.040** 0.020 0.040** 0.020

Number of Bedrooms 0.012 0.012 0.012 0.012

Number of Fireplaces 0.079*** 0.011 0.079*** 0.011

Central Air 0.048** 0.019 0.048** 0.019

Overall Condition 0.102*** 0.026 0.102*** 0.026

Square Footage 0.000*** 0.000 0.000*** 0.000

Age -0.001** 0.000 -0.001** 0.000

Age2 0.000 0.000 0.000 0.000

Agriculture -0.405 0.436 -0.405 0.436

Two Family -0.094*** 0.034 -0.094*** 0.034

Three Family -0.165** 0.066 -0.165** 0.066

Rural with Acreage 0.351*** 0.044 0.351*** 0.044

Estate 1.380*** 0.385 1.380*** 0.385

Seasonal -0.815*** 0.188 -0.815*** 0.188

Mobile Home -0.741*** 0.215 -0.741*** 0.215

Multi Residences 0.304*** 0.048 0.304*** 0.048

ln Dist. to Nearest Streams 0.003 0.006 0.003 0.006

ln Dist. to Nearest Lake 0.006 0.009 0.006 0.009

ln Dist. to Nearest Pond -0.008* 0.005 -0.008* 0.005

ln Dist. to Nearest DEC Land 0.002 0.012 0.002 0.012

ln Dist. to Nearest DEC Trail 0.001 0.005 0.001 0.005

ln Dist. to Nearest Towncenter 0.016** 0.007 0.016** 0.007

ln Dist. to NYC -0.155** 0.063 -0.155** 0.063

ln Dist. to Albany -0.052 0.107 -0.052 0.103

ln Dist. to Nearest Street (35-45 mph) 0.014*** 0.005 0.014*** 0.005

ln Dist. to Interstate - 90 -0.055 0.084 -0.055 0.080

ln Dist. to Interstate - 87 -0.006 0.008 -0.006 0.008

ln Dist. to Nearest Railroad 0.017 0.011 0.017 0.011

ln Dist. to Hudson River -0.027** 0.013 -0.027** 0.013

ln Dist. to Nearest Bridge over Hudson River 0.009 0.010 0.009 0.010

Acreage of nearest DEC Lands -0.000 0.000 -0.000 0.000

Acreage of nearest Lake 0.000* 0.000 0.000* 0.000

Acreage of nearest Pond -0.001 0.001 -0.001 0.001

Stream Intersects Property 0.073*** 0.009 0.073*** 0.009

Pond Intercepts Property 0.087*** 0.022 0.087*** 0.022

Lake Intercepts Property 0.147 0.100 0.147 0.100

Elevation in Meters 0.001*** 0.000 0.001*** 0.000

Year and Month Dummies Yes Yes Yes Yes

_cons 15.195*** 1.480 15.195*** 1.465

note: *** p<0.01, ** p<0.05, * p<0.1

2K Buffer 5K Buffer

34

Block Level Results – 3-6 Month Flood Window

Variables coef se

Property in 100-year Flood Zone -0.073** 0.029

Property in 500-year Flood Zone -0.031 0.024

Acreage of Wetlands within 2 Kilometers

Acreage of Wetlands within 5 Kilometers 0.000 0.000

Acreage of Wetlands(2k) * 100yr Flood Zone

Acreage of Wetlands(5k) * 100yr Flood Zone

Flood Occurred 0-3 Months Prior to Sale -0.006 0.015

Flood (0-3 Months) * 100yr Flood Zone -0.235*** 0.084

Flood (0-3 Months) * Flood Zone * Wetlands(5k) 0.000*** 0.000

Flood Occurred 3-6 Months Prior to Sale -0.008 0.012

Flood (3-6 Months) * 100yr Flood Zone -0.042 0.072

Flood (3-6 Months) * Flood Zone * Wetlands(5k) 0.000 0.000

Acres 0.003* 0.001

Number of Kitchens -0.078*** 0.016

Number of Full Bathrooms 0.097*** 0.025

Number of Half Bathrooms 0.040** 0.020

Number of Bedrooms 0.012 0.012

Number of Fireplaces 0.079*** 0.011

Central Air 0.048** 0.019

Overall Condition 0.102*** 0.026

Square Footage 0.000*** 0.000

Age -0.001** 0.000

Age2 0.000 0.000

Agriculture -0.404 0.436

Two Family -0.094*** 0.034

Three Family -0.165** 0.066

Rural with Acreage 0.351*** 0.044

Estate 1.381*** 0.385

Seasonal -0.815*** 0.187

Mobile Home -0.741*** 0.214

Multi Residences 0.305*** 0.047

ln Dist. to Nearest Streams 0.003 0.006

ln Dist. to Nearest Lake 0.006 0.009

ln Dist. to Nearest Pond -0.008* 0.005

ln Dist. to Nearest DEC Land 0.002 0.012

ln Dist. to Nearest DEC Trail 0.001 0.005

ln Dist. to Nearest Towncenter 0.016** 0.007

ln Dist. to NYC -0.155** 0.063

ln Dist. to Albany -0.052 0.103

ln Dist. to Nearest Street (35-45 mph) 0.014*** 0.005

ln Dist. to Interstate - 90 -0.055 0.080

ln Dist. to Interstate - 87 -0.006 0.008

ln Dist. to Nearest Railroad 0.017 0.011

ln Dist. to Hudson River -0.028** 0.013

ln Dist. to Nearest Bridge over Hudson River 0.009 0.010

Acreage of nearest DEC Lands -0.000 0.000

Acreage of nearest Lake 0.000* 0.000

Acreage of nearest Pond -0.001 0.001

Stream Intersects Property 0.073*** 0.009

Pond Intercepts Property 0.087*** 0.022

Lake Intercepts Property 0.147 0.100

Elevation in Meters 0.001*** 0.000

Year and Month Dummies Yes Yes

_cons 15.192*** 1.468

note: *** p<0.01, ** p<0.05, * p<0.1

5K Buffer

35