the value of trees, water and open space (Netherlands).PDF

Landscape and Urban Planning 48 (2000) 161±167

The value of trees, water and open space as re¯ected

by house prices in the Netherlands

Joke Luttik *

Alterra, Green World Research, P.O. Box 125, 6700 AC Wageningen, Netherlands

Abstract

An attractive environment is likely to in¯uence house prices. Houses in attractive settings will have an added value over

similar, less favourably located houses. This effect is intuitively felt, but does it always occur? Which environmental factors

make a location an attractive place to live in? The present study explored the effect of different environmental factors on house

prices. The research method was the hedonic pricing method, which uses statistical analysis to estimate that part of a price due

to a particular attribute. Nearly 3000 house transactions, in eight towns or regions in the Netherlands, were studied to estimate

the effect of environmental attributes on transaction prices. Some of the most salient results were as follows. We found the

largest increases in house prices due to environmental factors (up to 28%) for houses with a garden facing water, which is

connected to a sizeable lake. We were also able to demonstrate that a pleasant view can lead to a considerable increase in

house price, particularly if the house overlooks water (8±10%) or open space (6±12%). In addition, the analysis revealed that

house price varies by landscape type. Attractive landscape types were shown to attract a premium of 5±12% over less

Keywords: Economic valuation; Trees; Water; Open space; House prices

1. Introduction

socio-economic value, the question is how to measure

this value. One of the links between economy and

ecology is found in the premium that houses in an

attractive, green setting attract over houses in a less

favourable location. This premium is an expression of

the socio-economic signi®cance of ecological factors

in a rural±urban setting. It is often felt that the socio-

economic value of ecological factors is not suf®ciently

re¯ected in policy priorities. A quanti®cation and

speci®cation of this value will support ecological

arguments in policy debate and urban±rural planning.

If the socio-economic value of ecological factors can

be demonstrated through a premium on house price,

this strengthens the position of existing green areas in

the policy decision process. It may thus act as a

Integrated decision making emerged as a major

concern from the workshop on urban±rural relation-

ships (Tjalingii, 2000). Decisions on land-use should

not only be motivated by economic (and social)

arguments, they should also include ecological moti-

vations. As a consequence, it is important to under-

stand the interaction between socio-economic and

ecological factors. In the context of urban±rural rela-

tionships, an obvious research topic is the socio-

economic value of ecological factors for residents.

No one would doubt that ecological factors have a

E-mail address: j.luttik@alterra.wag-ur.nl (J. Luttik)

PII: S 0169-2046(00)00039-6

162

J. Luttik / Landscape and Urban Planning 48 (2000) 161±167

counterbalance in urban expansion plans, when urban

development threatens green areas and open spaces.

One of the most important reasons for the suscept-

ibility of green areas and open spaces to urban pres-

sures is that they are not articulated in monetary terms

(More et al., 1988). Decision-makers compare eco-

nomic factors like contribution to the tax base and

employment or the value added to the local economy

against the value of environmental factors. By expres-

sing the latter in monetary terms they become com-

parable to the former. This will put more weight on

environmental factors in the decision making process

(although by no means all environmental values can be

put into monetary terms).

Also in urban±rural planning, insights in the socio-

economic value of green areas and open spaces will

help to optimise socio-economic and ecological fac-

tors simultaneously. One of the issues where these

insights may contribute is design of new urban areas,

in particular where the distribution of green areas

(including water bodies) and houses over new urban

areas is concerned. The premium on house price may

be used as the guiding principle for optimising the

socio-economic value of ecological factors. Public

®nance is the main source of ®nance for green areas,

but it is conceivable that future residents and/or urban

developers will ®nance the creation of new green

areas. In the context of increasing demand for green

areas, which is not met by an increase in public

®nance, this is exactly what the Dutch government

is looking for: ®nancing possibilities from private

sources. Interesting examples of private ®nance for

green areas are the (experimental) New Rural Life-

style Estates in the Netherlands (van den Berg and

Wintjes, 2000). On these estates, which are to be

founded on former agricultural land, development

rights are provided in exchange for the production

of new nature and landscape. In the case of private

®nance of green areas, a careful analysis of the value-

increasing effect of attractive, green settings on house

price is of course particularly important.

This study aims to clarify how, when and to what

extent the value-increasing effect on house price arises

in the Netherlands. These issues have been studied

before, in particular in the US and the UK (e.g.

Morales, 1980; More et al., 1988; Anderson and

Cordell, 1988; Garrod, 1994; Powe et al., 1995). In

the Netherlands. however, the empirical evidence is

scarce. Since the urban±rural settings in the UK and

the US differ considerably from those in the Nether-

lands, the empirical ®ndings cannot be translated to

the Dutch situation. In the Netherlands, urban pressure

in the Randstad Ð which covers the major cities

Amsterdam, The Hague. Rotterdam and Utrecht, as

well as a number of smaller towns and villages Ð is

particularly high. This coincides with a high demand

for green areas and open space for recreational pur-

poses. Consequently, the integrated development of

urban and green plans is a relevant policy issue in this

area. Therefore, the emphasis on research areas in this

study is in the Randstad, which is represented in the

study with live research areas. To investigate the

regional impact, three research areas outside the

Randstad were studied. They are spread over the

country, located in the centre, the north and the south.

2. Method

Broadly speaking, there are two ways to establish

the value-increasing effect of a speci®c housing attri-

bute. The ®rst way is to ask the people concerned Ð

for example residents or estate agents Ð how they

value a particular attribute. A second way is to derive

the value from actual behaviour. The hedonic pricing

method (HPM) is an example of the latter. Assuming

that houses are valued for their several attributes,

housing transactions are examined to estimate that

part of a price due to a particular attribute. There are

two categories of attributes: the structural character-

istics of the house Ð like plot size, house type or

number of rooms Ð and the locality, which may be

valued positively or negatively. Although our analysis

focuses on environmental attributes, which make tip a

relatively small part of total house price, the HPM

requires that all attributes that affect house price are

included in the analysis.

In 1995, a pilot study based on the HPM was carried

out in Apeldoorn, a medium-sized town in the east of

The Netherlands (Fennema et al., 1996). This study

analysed 106 house transactions in a relatively new

district, which is built round a park. The study demon-

strated that location within 400 m of the park attracted

a premium of 60% over houses located outside this

zone. In addition, a house with a park view appeared to

attract a premium of 800. The results were encoura-

J. Luttik / Landscape and Urban Planning 48 (2000) 161±167

163

ging, because they were consistent and con®rmed the

expectation that green has a value-increasing effect on

house price. But could this effect also be demonstrated

in other areas, for different house types and other

environmental factors? This article presents the results

of both the follow-up study which tries to answer these

questions, and the pilot study.

Nearly 3000 house transactions were studied to

estimate the effects of environmental attributes on

transaction prices. We were able to use a huge data

set Ð featuring transaction prices and various struc-

tural house characteristics Ð that was provided by the

Dutch Association of Estate Agents (NVM). Informa-

tion on environmental and other location factors was

drawn from maps, and complemented by speci®c,

detailed information on the locality gathered by visit-

ing each house in the sample. This was necessary to

get a complete picture of the view from each house in

the sample and of disturbing factors like traf®c noise.

The accessibility of green areas was examined by

bicycle. Thus obstacles not apparent from maps could

be experienced.

To minimise the impact of in¯ation, a period char-

acterised by price stability was selected, i.e. the period

1989±1992. Since maintenance level is notably dif®-

cult to measure, only transactions in houses built after

1970 were included. Thus we could safely assume that

the in¯uence of maintenance levels was negligible. A

large sample is a pre-condition for a reliable result

from HPM analysis. Therefore, every house built after

1970 for which an NVM transaction was recorded in

the period 1989±1992 was included in the sample.

Since the housing market is highly segmented, house

transactions were studied for each research area sepa-

rately.

The analysis was performed in two stages. Firstly,

the house price due to structural housing attributes was

estimated in a linear regression analysis. Subse-

quently, we assumed that the difference between this

value and the actual transaction price could be

ascribed mainly to difference in locality. Locality

refers to not only to environmental amenities, but also

to schools, traf®c noise, view of apartment buildings,

motorways, shops, public transport or other public

facilities. The ratio of the estimated price and the

actual transaction price is referred to as the location

indicator Ð which was calculated as the difference

between the two values expressed as a percentage of

the estimated value. The location-indicator is linked to

location variables in a second linear regression ana-

lysis. Since the research is focused on environmental

factors, only the results from the second stage are

presented and discussed in the following.

3. Hypotheses and results

The central hypothesis is that houses in an attractive

setting attract a premium over houses in a neutral

setting. Green areas. water bodies, open space and

attractive landscape types are aspects of an attractive

setting. Since these are valued differently by residents,

for example because they differ in use value, they will

affect house prices differently. The selection of

research areas assured an analysis of the in¯uence

of a wide range of green area types, water bodies, open

space and landscape types. Not only do they differ in

age, function and type, they also occur on different

scale levels: from small, decorative strips of green and

small canals to large parks and lakes.

Table 1 summarises the tested hypotheses and the

results for environmental factors. The following

example illustrates how the table reads. The ®rst line

presents the results for the hypothesis ``a view of a

green strip has a value±increasing effect on house

price''. These hypotheses was tested in six cases (in

the two other cases, the situation did not allow for a

test of this hypothesis); in three cases the variable was

signi®cant, in three cases it was not. The premium in

the three signi®cant situations amounted to 4% (one

case) and 5% (two cases).

The table shows that the impact of green areas was

ambiguous; in many cases, the hypothesis that a green

structure attracts a premium had to be rejected. The

effect of water bodies and open space could be

demonstrated in almost every instance. Attractive

landscape types were shown to attract a premium over

less attractive landscape types (monotonous agrarian

landscapes).

4. Combinations of results

In each research area, the impact of a different set of

environmental factors was examined. Since factors

may interact, it is important to test the effect of the

164

J. Luttik / Landscape and Urban Planning 48 (2000) 161±167

Table 1

Summary of results for environmental factors

Feature

Significant

Not significant

Not tested

Premium

In the residential area

Green strip

View of

3 cases, n962

3 cases, n1442

2 cases, n409

4%, 5%, 5%

Park

View of

2 cases, n456

6 cases, n2357

7%, 8%

Vicinity

1 case, n112

1 case, n2701

Canal

Facing garden

1 case, n297

7 cases, n2516

View of

2 cases, n391

6 cases, n2422

4%, 5%

Lake

Facing garden

2 cases, n443

6 cases, n2370

11%, 12%

View of

2 cases, n443

6 cases, n2370

8%, 10%

Vicinity

2 cases, n443

6 cases, n2370

5%, 7%

Bordering of residential area

Park

Vicinity

1 case, n297

3 cases, n1031

4 cases, n1485

12%

Lake

Vicinity

3 cases, n1166

5 cases, n1647

5%, 7%, 10%

Open space

View of

2 cases, n929

6 cases, n1884

6%, 12%

Regional features

Woods

Presence

2 cases, n890

6 cases, n1923

8%, 12%

Lake

Presence

1 case, n336

7 cases, n2477

Diversity of landscape types

Presence

1 case, n593

7 cases, n2220

various factors simultaneously. For example, in a town

surrounded by an attractive wooded landscape, the

impact of a green area bordering the residential area is

likely to differ from a town without attractive regional

features. When several environmental characteristics

are signi®cant in a certain research area, the effects are

additional. Consequently, if a house has a garden

bordering water, this implies a view of a lake, which

in turn implies that there is a lake in the vicinity. Thus,

if the three effects are all signi®cant, there are three

premiums on house price. In the following, the three

most striking cases are discussed. In each case, a set of

different environmental factors is highlighted. These

cases are illustrative both for approach and results.

not be demonstrated. Irrespective of zone de®nition,

the variable `close to the woods' was not signi®cant.

The effect of the lake, however, emerged very clearly.

Location in the district with the lake, which comes to

the same thing as location within 1000 m of the lake,

attracted a premium of 7% over location in the other

two districts. A water view raised price by an extra

10%, whereas a garden bordering on water attracted a

premium of 11%. This means that the price of a house

with a garden bordering on water is on average 28%

higher than the price of a house in one of the other two

districts. Fig. 1 illustrates the results for Emmen.

4.2. Case Apeldoorn

4.1. Case Emmen

In Apeldoorn, a medium-sized town in the east of

the Netherlands, 102 house transactions were studied

to assess the effect on house prices of a park located

right in the middle of the district `De Maten' (Fen-

nema et al., 1996). The distance from the park to the

edge of the district amounts to 800 m. An effect of

location close to the park, i.e. within 400 m (walking

distance), could be demonstrated Ð a premium of 6%.

On top of this, a view of the park was shown to attract

an extra price increase of 8%. View of a multi-storey

apartment building was a negative factor, decreasing

house price by 7%. Thus, price difference between

houses could accumulate to 21%, which represents the

Emmen is a medium-sized town in the northeast of

the Netherlands, with three districts built after 1970.

Two districts are built on sandy soil. They have woods

bordering the residential area. The third district is

designed around a new lake, on former farmland.

The new district facing the lake and a part of the lake

itself have been developed simultaneously. The sam-

ple consists of 282 transactions in houses, more or less

evenly distributed over the three districts. Contrary to

expectations, a positive effect of location close to the

woods, i.e. on the attractive side of the districts, could

J. Luttik / Landscape and Urban Planning 48 (2000) 161±167

165

Fig. 1. The effect of a lake on house prices in Emmen.

difference in prices of comparable houses between

least favourable (ÿ7%) and most favourable (68%)

locations, as illustrated in Fig. 2.

the most favourable location attracts a premium of

29% (see Fig. 3).

4.3. Case Leiden

5. Concluding remarks

Leiden is one of the larger towns in the west of the

Netherlands. In Leiden, 336 house transactions were

studied spread over several town districts in the north,

the west and the south. The central assumption in

Leiden was that the attractive landscape with water

features north of Leiden would attract a premium over

less attractive settings. The distance between the dis-

trict in the north of Leiden and other houses in our

sample is substantial Ð it varies between 3 and 5 km.

The hypothesis could not be rejected and the estimated

premium for an attractive landscape type was 7%. Two

other factors were shown to have an impact on house

price in Leiden: traf®c noise and a nice view. Traf®c

noise was shown to exercise a negative in¯uence

(ÿ5%). A nice view could be either a water view or

a view of open space, or both. Thus, the most favour-

able setting in Leiden is in the northern district (7%

for attractive landscape with water features), an open

view (9%) and a water view (8%) Compared to the

least favourable location characterised by traf®c noise,

Interpreting the results, some limitations of the

HPM should be kept in mind. The results only apply

to districts built after 1970 and they are only valid

within the context of the set of environmental factors

they where derived in. Great caution is required when

results are transferred to other areas or types of green.

Naturally, the premium is relative; it applies to a group

of houses in relation to a speci®ed group of other

houses. The essence of the method is a comparison of

situations with and situations without a speci®c attri-

bute. Consequently, the value of a speci®c attribute

can only be tested if suitable situations with and

without can be found. For example, if a whole district

is nice and green, the value-increasing effect of green

in the residential area cannot be tested in this district.

Another Ð otherwise comparable Ð district, which is

not nice and green, is needed. Since the house market

is highly segmented, the two districts should be found

within the same segment of the house market. This

caused dif®culty in the selection of suitable research

Fig. 2. The effect of a park in Apeldoorn.

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