Download Tender Document

Research Tender
Title:
Hedonic Pricing Study to Determine whether Energy Efficiency Ratings and Council
Tax Differentials Impact on Prices in the Domestic Buildings Sector
Reference No: TRN 297/11/2011
15 December 2011
Department of Land Economy
19 Silver Street,
University of Cambridge
Cambridge, CB3 9EP
Phone: 01223 337137
School of Real Estate & Planning
Henley Business School
University of Reading
Whiteknights
Reading
RG6 5UD
Section 1: Understanding of the requirement and objectives addressed
A: Introduction
Both internationally (via UN-led international protocols on carbon reduction) and nationally (via
recently enacted legislation to legally limit carbon emissions), the UK has led action on climate
change. The building stock is recognised as a major contributor to carbon emission with the domestic
stock estimated to be responsible for approximately 27% of UK carbon emissions1. There has been
substantial government investment in the development and deployment of technologies to reduce
demand for building-related energy. Over the past decade, Energy Efficiency Commitments (EEC1
and EEC2), Warm-front, and the Carbon Emissions Reductions Target (CERT) have encouraged
domestic occupiers to invest in energy saving technologies. The ‘Green Deal’, which the Government
is currently consulting on, is the latest and most extensive initiative. It seeks to work with energy
suppliers and encourage investment in renewable energy generation as well as energy reduction.
Whilst recent DECC research has shown2 that there are demonstrable savings from investment in
energy saving technologies, there is a lack of research into whether these cost savings are reflected in
capital prices and rents paid. The same is true for property-related tax payments. Since the
introduction of Energy Performance Certificates (EPCs) in 2008 and Council Tax bandings in 1993,
very little research exists into whether these dwelling attributes are significant price
determinants. Identifying such an effect, from what might be regarded as second order demand
attributes, relies on the hedonic price modelling of comprehensive and well-specified data sets.
Over the course of several real estate pricing studies, researchers at the Universities of Cambridge and
Reading have acquired excellent data procurement, database assembly and price modelling skills in
this field. Our most recent research focuses on the price impact of EPCs, BREEAM, LEED and
Energy Star ratings on the price of non-domestic buildings. Combined with our understanding of
energy modelling in the domestic building stock, we believe that our price modelling experience
delivers the required skill-set for a hedonic pricing study in the domestic building sector.
B: Background and context
The changing preferences and attitudes of market participants and new environmental regulation are
having an increasing impact on the operation of property markets. Among property occupiers,
investors and professionals, there has been a growing awareness of the contribution that the property
1
http://www.direct.gov.uk/en/HomeAndCommunity/BuyingAndSellingYourHome/Energyperformancecertificat
es/DG_177026
2
National Energy Efficiency Data-Framework: Report on the development of a data-framework and initial
analysis, June 2011, Department of Energy & Climate Change
2
industry can make towards reducing the environmental effects of business. Similar to many other
product markets, voluntary (albeit often prompted by government) environmental certification
schemes have emerged in most mature property markets. A range of acronyms have appeared for
similar property eco-certification schemes, e.g. BREEAM (UK), NABERS (Australia), CASBEE
(Japan), HQE (France), LEED (USA) and DGNB (Germany). In addition to these voluntary ecolabels, Energy Performance Certificates (EPCs) and Display Energy Certificates (DECs) are examples
of mandatory energy labels that have been introduced across the European Union.
Our understanding of the objectives of this project rests on the assumption that EPCs and related
policies aim to provide information to consumers or users about the environmental or energy
performance of a product. The indirect objective is to then change consumption and investment
choices, suppliers’ production outputs and, as a result, the level of environmentally harmful
emissions. Assuming that energy efficiency is a salient attribute for consumers, we would expect that
energy efficiency is reflected in rental values and capitalised into house prices via increased demand
for properties with lower running costs and lower exposure to future energy price risk. We expect that
any energy efficiency premium identified in the hedonic analysis is likely to be a relatively small
component of the overall transaction price. With regard to policy interventions, it is thus pertinent to
explore whether council tax levels are also capitalised into house prices and if so, assess the potential
impact of a policy intervention that aims to differentiate council tax bands not only by value but also
by the energy efficiency of a dwelling.
There is a growing body of research related to this topic. In our database of empirical studies of the
price effects of environmental or energy certification on property prices, we have documented over 20
papers that have been put into the public domain since 2008 (see Appendix 1 for a review of relevant
studies). A number of observations can be made. Most of the studies have been on US offices. The
vast majority of studies have found a positive (albeit variable and inconsistent) effect of
environmental or energy certification on rents and sale prices. Usually due to incomplete information,
the studies differ in sample size and composition, econometric model specification, outcome variables
(appraisals, prices), handling of data errors and control for location effects. Outside of the US,
preliminary studies presenting evidence of the effect of eco-certificates and/or energy efficiency on
pricing have recently been published for the UK, Netherlands, Germany, Japan and Singapore.
Finally, all the studies are snapshots in time. If market penetration of environmentally certified or
highly energy efficient buildings (supply) continues to increase and the attitudes of investors and
occupiers continue to change (demand), the price effects of environmental certification on buildings
will also continue to evolve.
3
Much more developed is the long established body of work, particularly in the US, investigating the
effects on house prices of variations in real estate taxes among different jurisdictions (see Cheshire
and Mills, 1999 for a review). Theoretically, it is accepted that, all else equal, land rents should
capture property tax differentials. There is substantive empirical evidence to support this hypothesis.
For instance, following the introduction of Proposition 13 in California in the late 1970s, Rosen
(1982) finds that jurisdictions with the largest decreases in tax experience highest house price growth.
However, a key mediating factor is scope of local services. Later work in Massachusetts found that
communities that were able to increase tax revenues more rapidly and to allocate more resources to
education spending experienced faster growth in property values (see Lang and Jian, 2005). In the
UK, there has been limited empirical investigation of the effect of property taxes upon property
prices. Bond, Denny, Hall and McCluskey (1996) attempted to model the dynamic relationship
between changes in non-domestic rates and rents for a sample of commercial properties. Whilst they
find some evidence to support a negative impact on rents of increasing rates bills, they find large
variations in lags between markets and, sometimes, rather implausible results. We are not aware of
any empirical studies that investigate the impact of Council Tax variations on local house prices
across the UK.
C: Key Objectives and Aims
It is expected that the research project will deliver:

A comprehensive dataset of property attributes, prices, local property taxes and energy
efficiency performance for a large sample of English residential properties.

An evaluation of the potential contribution and limitations of hedonic analyses to estimating
the price effects of Council Tax levels and EPC ratings on English residential properties.

A comprehensive review of existing research on the effects of local property tax levels and
environmental performance on property prices.

Estimates of the effects of local council tax levels and EPC labels on residential property
prices using standard cross-sectional hedonic modelling.

Estimates of the effects of local property tax levels and energy labels on residential property
prices using repeat sales modelling.
D: Data Requirements
The underlying premise of hedonic analysis is that values of numerous attributes of a multi-faceted
economic good are reflected in either capitalised prices or annualised rents. Therefore, dwelling
occupiers receive utility from each of the many attributes of a good, in this case the housing unit.
Dwelling prices are hedonic in that they represent a bundle of attributes such as location advantages,
4
space, quality of product etc. The number of hedonic attributes could, theoretically at least, be large
in number.
However, often a small number of key characteristics tend to be the key price
determinants. When examining the impact that EPCs and Council Tax might have on prices, it is
essential that the key price determinants are identified and controlled for. Therefore, to conduct the
hedonic regression analysis, the following attribute data are required:

transaction price

transaction date

size (floor areas and/or number of bedrooms)

type (detached, semi, terraced etc.)

age (year built or suitably constructed age bands)

location (exact address)

changes (inflation/deflation) in house prices

location area attributes

property tax information
A potentially significant variable that is missing from the list above is property condition. It is
possible that older dwellings that have been refurbished or are well-maintained (new attic, extensions,
new boiler, etc.) are going to have higher EPC ratings than poorly maintained buildings. Data on
condition is not generally available in the UK at the dwelling level other than via the sample-based
English Housing Survey. The Valuation Office Agency, widely regarded as the custodian of the most
comprehensive set of dwelling attribute data, does not have up to date, detailed information on
condition.
Property tax information is not publicly accessible in England and Wales. It is possible to obtain
Council Tax bands for small batches of dwellings via the Valuation Office website but not for large
samples of several thousand dwellings. It is possible to obtain information from Communities and
Local Government on the amount of Council Tax that each local authority charges on an annual
basis. These amounts are expressed as average Council Tax for Band D; other bands can be
calculated as they relate to Band D in fixed ratios. Using this data set, it is possible to investigate
whether there is any impact on price paid as a result of variations in local authority Council Tax
levels.
Sources of data will need to be investigated in the first stage of the research. Key data holders are
listed in Table 1 but more may be identified during Stage 1 of the project. The Land Registry record
the address, property type and price paid for all freehold and most leasehold transactions in England
5
and Wales and this information has been publicly available since 1995. EPCs were introduced in
September 2008 and therefore the first stage of this project would investigate the feasibility of
obtaining transaction data from the housing market from that date onwards. The Land Registry data
set is also commercially available in bulk form and several organizations have acquired the data and
added further variables. Our enquiries in respect of this project and in previous work we have
undertaken have shown Calnea to be a key source of real estate data, particularly regarding the
additional data that they have added to the Land Registry data. We have obtained a quote from
Calnea covering up to 1 million transaction prices with key property attributes such as type, number
of bedrooms, year built and geo-coordinates matched to these prices. The company is now part of the
Landmark Group and the research team has established relationships with staff at both Calnea and
Landmark.
Table 1 – Key data sources
Data owner
Legal status
Data set
Data items
Land Registry
An Executive Agency of
the Lord Chancellor’s
Department
An Executive Agency of
HMRC
Transaction prices
Title reference, address, price,
quarter date of transaction, tenure
Council Tax list
Unique ID, Address, Council Tax
band
Calnea
Private sector company
Property attributes and
prices
Experian
Public limited company
Landmark
Subsidiary company of
the Daily Mail General
Trust
Various socioeconomic and
demographic data sets
Quest
Property type, number of
bedrooms, year built (1995
onwards), full address, transaction
prices
Wide range of modeled socioeconomic and demographic data
Communities
and Local
Government
Government Department
English Indices of
Deprivation
Communities
and Local
Government
Government Department
English Housing
Survey
VOA
Property attributes collected by
mortgage valuers when assessing
the value of dwellings for lending
purposes
LSOA-level data on income,
employment, health and
disability, education, housing and
crime
Dwelling level data on household
characteristics and physical
attributes
Modelled data for property attributes and socio-economic characteristics of dwelling occupants are
also available commercially from the information company Experian. Variables include house type,
number of bedrooms, tenure, household income, number of occupants, age of main householder and
length of residence. Experian collects data from a range of sources. The 2001 Census provides
details of the neighbourhood and this is supplemented by Experian’s consumer survey and
administrative data. The Experian model then derives variables for each address. In addition, Output
6
Area level demographic data are available from the Office for National Statistics.
Property attribute data are collected and maintained by the Valuation Office Agency (VOA).
Whereas Experian property attributes are modelled, the VOA, which is responsible for allocating
homes in England and Wales to an appropriate Council Tax band, maintains a property attribute
database and key attributes such as dwelling type, age and size are available for nearly all dwellings.
However, these attribute data are not publicly available; only Council Tax bands for individual
addresses are available. These data are free of charge from the VOA website but only downloadable
in small batches. The ability to acquire and match Council Tax data from the VOA and match to the
sample of transaction data would be investigated in the first stage of the project. Information on the
private-rented sector and, in particular, rents paid is not readily available in England and Wales.
Sources will be investigated including the Association of Residential Letting Agents, Housing
Associations and the Local Government Association.
E: Research Strategy and Analytical Framework
This study aims to test whether energy efficiency performance proxied by EPC ratings is capitalised
into house prices. It further seeks to identify whether the level of running costs of a property (proxied
by EPC rating and local authority Council Tax costs) affects the transaction price of a property. The
method of choice for studying the contribution of individual traits or characteristics of goods to their
price is hedonic price analysis. This method will form the backbone of our empirical investigation
into the research questions outlined above. In particular, this method allows us to control for a number
of confounding factors such as age, size and location of a property in order to isolate the unique
contribution of energy efficiency and property tax liability to house price.
The analytical approach to determine the hedonic contribution of EPC rating and council tax to price
is faced with the following problems which must be addressed within given time and resource
constraints:

First, a common problem is lack of control for unobserved heterogeneity that can arise from
the local area. If these un-observables (e.g. attractive garden) are correlated to the observed
attributes, then the estimates are biased. One way to address the issue is to include local area
fixed effects (specified as dummy variables) in the model specification, assuming that
correlated un-observables are time-invariant. In our cross-section model, we will explicitly
control for such unobserved effects.

Second, it is possible that a proportion the property units in the sample may have undergone
7
physical changes due to renovation. A renovation may affect both the price and the EPC
rating.
One way to address this concern is to form a sample of properties which are sold
twice in the sample period i.e. repeat sales units for which it is reasonable to assume that
those have not undergone significant changes between two transaction dates in terms of
physical attributes. Therefore, we will also perform a hedonic analysis with only repeat sales
transactions. Specifically, difference in sales prices between two transaction dates will be
regressed on a set of property and unit attributes including the constant EPC ratings. This
exercise will be able to estimate the extent to which growing awareness of EPC ratings and
energy efficiency has affected levels of capital growth in residential dwellings.

Third, an important risk in sale price analysis is that some properties may be more likely to be
offered for sale than others (sample selection bias). This may be due to a property’s
characteristics or the occupying household’s characteristics or a combination thereof. For
example, less attractive properties might have a larger propensity to be sold since they are
more likely to be occupied by transient and younger households. If this were the case, we
might seriously underestimate or overestimate the effect of energy efficiency on price. The
direction of this bias would be dependent on the differential characteristics of the households
and properties occupying energy-efficient properties compared to the control group or general
population. Appropriate econometric testing (two-step Heckman correction procedure) will be
conducted to address this concern.
If we find during Stage 1 that it is not feasible to obtain Council Tax payment or billing data at
dwelling level, we propose an alternative approach that focuses on relative differences in Council Tax
levels for ‘Band D’ properties across local authorities rather than differences across properties. This
approach has the desirable property that it circumvents the inherent causality problem of actual
Council Tax paid. This is due to the fact that more expensive dwellings incur higher Council Tax but
it is primarily the value of each dwelling that determines the level of Council Tax paid and to a much
lesser extent (or possibly even not at all) the other way around. More specifically, we will create a
variable that expresses the indexed level of Band D Council Tax for each local authority as a
percentage of the national average level. This variable will allow us to test in the hedonic regression
model whether variations in Band D Council Tax both across local authorities and within the same
local authority over time have any significant effect on prices.
In summary, our analytical approach is two-pronged: we propose a repeat sales framework to address
concerns about changes in unit attributes and a cross-section framework to address potential issues
with selective samples and also to act as robustness checks. These regression-based models will be
used to estimate the effect of EPC ratings and Council Tax levels on property prices and are suitable
8
for the task at hand in that they minimise potential selection and estimation biases that could
invalidate the results.
Section 2: Demonstration of experience in hedonic data analysis
Appendix 2 contains a list of publications by the research team directly related to the proposed
research project. Among the team members, there is significant experience in conducting large-scale
hedonic studies. Dr Fuerst’s work, in particular, has been at the cutting edge of applying hedonic
regression procedures to estimating the price effects of environmental performance on real estate
pricing. Commissioned by DECC, Dr Wyatt has been involved in research conducting econometric
modeling of domestic energy consumption for English households.
Dr Nanda has previously
undertaken a major hedonic analysis to study effects of school district performance on property values
in US using transaction data. Having dealt with a large transaction dataset with potential issues and
risk mitigation for such hedonic analyses, Dr Nanda will oversee database management and
regression analysis. The data collection process as well as the application of these methods will be
conducted by a team of researchers at the Universities of Cambridge and Reading with long-standing
expertise in working with these complex and sensitive analytical tools. Brief biographical details of
the members of the research team are provided below.
Dr Franz Fuerst (FF) is the Principal Investigator of this project and will be responsible for ensuring
the overall success of the proposed research as well as designing and applying the econometric
modelling framework. He has conducted a number of research projects involving real estate market
analysis and economic forecasting for public and private sector clients. He is currently Reader in
Housing and Real Estate Finance in the Land Economy Department of Cambridge University and a
Visiting Professor at the IREBS Centre of Sustainable Real Estate. He is also the Cambridge
University Land Society Fellow (2011-14). Previous positions include Reader in Real Estate
Economics at the University of Reading, Senior Consultant at BNP Paribas Real Estate and Research
Fellow at the City University of New York. Main research interests include 'green' real estate
economics, financial analysis of sustainable investments, portfolio and risk management, and real
estate market forecasting as well as spatial economics. His research on the pricing of sustainable
features in commercial properties is widely acknowledged as being among the first studies of the
subject. His research was published in a number of top-ranked academic journals including
Ecological Economics, Environment & Planning A, Energy Policy, Applied Economics, Journal of
Air Transport Management, Real Estate Economics, Journal of Real Estate Research and Journal of
Portfolio Management and he has won several best paper awards, most recently the 2011 Emerald
9
Outstanding Paper Award (jointly with Patrick McAllister).
Professor Pat McAllister is a co-investigator of this project and will be responsible for interpretation,
analysis and reporting of the research outputs. He is currently Professor of Real Estate Appraisal at
the School of Real Estate and Planning at the University of Reading. His main research interests
include real estate and sustainability, financial modeling of real estate developments and the pricing
and appraisal of real estate assets. His research was published in a number of top-ranked academic
journals including Ecological Economics, Environment & Planning A, Environment & Planning B,
Energy Policy, Real Estate Economics, Journal of Real Estate Finance and Economics and Journal
of Property Research.
Dr Anupam Nanda (AN) is a co-investigator of this project and will be responsible for database
management, empirical analysis and report writing. He has previously undertaken a major hedonic
analysis with housing transaction data to analyse the effect of school quality on US house prices. He
is currently working as Lecturer in Real Estate Economics and Finance, and Associate Programme
Director for full-time MSc Real Estate at the University of Reading, UK. Previously, he worked with
the Market Intelligence group of Deloitte & Touche in Mumbai. He was at the National Association
of Home Builders (NAHB) in Washington DC, as Senior Research Economist, where his
responsibilities included developing and implementing housing market research studies and was a
member of the team forecasting state and metro area housing markets in US. Prior to joining NAHB,
he worked as a graduate research assistant at the University of Connecticut, USA. He has also taught
undergraduate Economics and Public Finance at the University of Connecticut during his graduate
studies. His research areas include: dynamic interactions across urban and regional markets; housing
market dynamics and forecasting; economic and econometric analysis of regulation; economics of
sustainability and corporate social responsibility; applied econometrics. His teaching areas include:
real estate economics; housing market analysis; quantitative methods. Dr Nanda holds PhD and MA
in Economics from the University of Connecticut. His research papers have been published in Journal
of Urban Economics, Journal of Real Estate Finance and Economics, and Cityscape etc.
Dr Peter Wyatt (PW) is a co-investigator of this project and will be responsible for information
management. This will involve the sourcing, sampling, collection, matching and administration of the
data sets that will be required to build and test the hedonic pricing model. Dr Wyatt is a Chartered
Valuation Surveyor who has conducted extensive teaching, consultancy and research in land
management and valuation. He is involved in international land management projects and has a
thorough knowledge of the theory and application of spatial analysis techniques for real estate
research.
Recent projects have investigated the energy performance of the domestic and non-
10
domestic building stock and he has worked with DECC on the development of a data framework for
the monitoring of real estate energy consumption.
Section 3: Explanation whether and how proposed approach would differ from existing hedonic
pricing studies
The Cambridge/Reading research team has a thorough understanding of the data and analysis issues
that challenge real estate research of this nature. With regard to Stage 1 of the research project, the
team will be able to apply its thorough knowledge of real estate and energy-related datasets. Given
the timescale, it is important that the team undertaking this research be able to draw upon existing
resources.
Previous projects undertaken by the team in this field provide these resources.
In
particular, we are able to draw upon strong links to real estate data suppliers including Landmark and
Calnea to acquire a large sample of dwelling prices and their associated attributes on which to conduct
the hedonic study. While the study will utilise standard hedonic tools and techniques, there will be
significant departure from existing practices. Most hedonic studies face perennial problems of
unobserved effects and sample selection biases. A combination of cross-sectional and repeat sales
analyses, as we are proposing in our proposal, will be able to disentangle these confounding issues.
Specifically, use of EPC labels and council tax as hedonic characteristics involves model specification
and econometric techniques to minimise biases arising from confounding factors at the unit and
neighbourhood levels.
The research team has wide experience of GIS and its application to real estate modeling. GIS will be
used to geo-code each dwelling. This will enable household and lifestyle attributes recorded at an
aggregated level (typically output area level or lower and medium level super output area level) to be
included in the hedonic analysis. Data sets such as the Office for National Statistics Local Area
Profiles and the English Indices of Deprivation published by Communities and Local Government
include several variables that may affect price paid for dwellings. Inclusion of these attributes may
prove to be useful in controlling for price effects.
Section 4: Extent to which risks are identified and addressed
Some potential areas and extent of risks can be identified at the onset. Table 2 summarises the risk
assessment and our plans for mitigation.
11
Table 2 – Risk analysis
Risk
Non-supply of data
Likelihood
Low
Impact
High
Bias in sample selection
Medium
Medium
Data matching
Medium
High
Data quality issues
Medium
Medium
Omitted variables
High
Low
Analytical risk
Low
High
No identifiable impact
Medium
Medium
Mitigation
Previous experience of project team. Can use one
of several private sector suppliers.
Appropriate econometric techniques (e.g. two-step
Heckman correction procedure)
Two main areas of concern; matching with
Landmark’s EPC data and other data sources that
may be used.
Experienced staff, time allowed for data cleaning
and processing, check data quality before
purchasing
Appropriate econometric techniques (fixed effects
and repeat sales method)
Appropriate techniques (mainly tests for selection
bias)
A neutral or negative finding regarding the impact
of energy efficiency (and Council Tax) on price
cannot be ruled out a priori due to the presence of
confounding factors at the unit and neighbourhood
levels. Appropriate econometric technique will be
employed to minimise such problems.
The most significant risk involves data availability, quality and compatibility. While data sources with
established track record have already been identified, there are potential challenges which may arise
when the data is used in practice.
It is usual for property attribute variables to be categorical (dwelling type and age band for example).
These variables can be transformed into dummy variables. Generally speaking, there are two ways of
handling location in a hedonic pricing model for dwellings. The first is to construct housing submarket areas that are relatively homogeneous in terms of price. The second is to include variables
that measure price sensitive location attributes such as distance variables (distance to town/city centre,
distance to main transport hub for example) and area variables (such as school catchment or
neighbourhood crime rate). The method for handling location depends on several factors including
data availability and size of sample. While we have recognised some potential areas of risk, we have
also identified appropriate tools for mitigation. These tools are well-established in the literature to
tackle such issues.
Section 5: Delivery plan: tasks and milestones
We have the capacity to complete this project within the specified timescale of early January 2012 to
the end of March 2012. We would also be able to provide weekly updates and produce brief output
reports at each stage. We outline the proposed work timetable in Table 3 and additional detail is
provided below.
12
Table 3 – Delivery plan
Tasks
Milestones
1. Data collection and
preparation
Survey of the literature; pilot study to
identify potential data problems
(Stage I)
Full sample data collection,
compilation and quality checks
(Stage II)
Hedonic regression estimation (Stage
III)
Empirical analysis/sensitivity tests
(Stage III)
Progress reporting (ongoing)
2. Data analysis
3. Reporting
January
February
March
Draft Report
(Stage IV)
Final Report and Dissemination
(Stage V)
Tasks
1.
Data collection and preparation
a)
Acquire core data set (property prices and attributes)
b)
Screen for data errors, data cleaning, database management (including mapping of
dwellings using GIS)
d)
Matching of dwelling-level database with other sources (e.g. ONS Neighbourhood
statistics, English Indices of Deprivation, etc.)
e)
2.
3.
Prepare database for external matching of EPC data and liaise with Landmark
Data analysis
a)
Develop and implement hedonic repeat-sales modelling framework
b)
Perform cross-section hedonic analysis with appropriate robustness checks
Reporting
a)
Liaise with DECC on weekly progress updates etc.
b)
Prepare and deliver final report
Two stages of data acquisition and matching are envisaged. Initially, we would acquire data for a
small, geographically specific, pilot sample of 5,000 dwellings. This pilot study would allow us to
examine the data sets for missing data, possible ambiguities and so on. Metadata describing the
13
applicability or fitness for purpose of each data set for a residential hedonic pricing model would be
an output from this stage. This will be in terms of:

Lineage and provenance: details of the source and history of the data and a description of the set
of processes that are used to produce a dataset. This would include how the data were digitised
and from what sources, when they were collected and by whom, and what steps were used to
process them.

Availability: to what extent are the data publicly available? There are several dimensions to this
question: at its simplest, it asks whether the data are released into the public domain at all. If the
data are released then is there a cost involved. Another important dimension is whether the data
are aggregated or anonymised prior to release and, if so, to what extent. Ideally, data should be
disaggregated to the individual property level.

Coverage: this aspect of fitness for purpose concerns the degree to which a data set conforms to
its specifications. Are all the records that should be logged present?

Content: the final and undoubtedly the most important aspect of fitness for purpose is the content
of the dataset.
Having identified and resolved where possible any ambiguities and data matching issues, a larger
sample of dwellings would be acquired. Using a three-way classification of size, type and age of
dwelling, it is envisaged that a sample of 500,000 records would be sufficient.
The sampling
procedure will aim to use sufficient properties in across England for an unbiased, uniform random
sample to be drawn. In order to assess the robustness of our analysis, the sample will include repeat
sales observations. The main model building and analysis stages can then begin.
.
14
Appendix 1 - Studies on price effects of energy and environmental labelling in real estate markets
Author(s) and
status
Miller. Spivey and
Florance (2008)
Published in
Journal of Real
Estate Portfolio
Management
Wiley, Benefield
and Johnson
(2010)
Published in
Journal of Real
Estate Finance and
Economics
Data
Approach
Findings on price differentials
‘Filtered’ sample of Class A
buildings (larger than 200,000 sq
ft, multi-tenanted, over five stories,
built after 1970) to compare to 643
ES buildings. 927 sale transactions
between 2003 and 2007.
Breakdown between LEED and ES
sale price observations is unclear.
Class A office buildings only.
46 metropolitan markets (25
markets for sales).
Hedonic OLS regression
for sale prices only.
Finds no statistically significant sales
price premium.
Hedonic OLS and 2SLS
regressions for rental and
occupancy rates.
Hedonic OLS and 2SLS find rental
differentials of 15-17% for LEED and 79% for ES.
Breakdown between LEED and ES
is unclear. We estimate 30 LEED
and 440 ES rental observations and
12 LEED and 70 ES sales
observations.
Control sample seems to
be other buildings in
same metropolitan area.
No controls for microlocation effects.
Hedonic OLS model of sales prices in
absolute form. Estimate sale price
premiums of $130 psf and $30 psf for
LEED and ES.
Controls for major
markets but none for
quality.
Other findings and potential
limitations
Occupancy rate is 2-4% higher for ES
compared to non-ES filtered sample.
Report 30% lower operating expenses
based on energy costs.
Hedonic OLS and 2SLS with occupancy
rate as dependent variable finds
occupancy rate differentials of 16-18%
for LEED and 10-11% for ES compared
to control group.
Key issue is location control.
Metropolitan area may be too large.
May be confusing location with
certification effects.
Potential omitted variable bias linked to
dual certified buildings not taken into
account.
Eichholtz, Kok and
Quigley (2010)
To be published in
American
Economic Review
Weighted average rents for 694
certified buildings. Sale prices for
199 certified buildings 2004-7.
Hedonic OLS regressions
for rental and sales
prices.
No statistically significant rental
premium for LEED. 3% rental premium
for Energy Star.
Breakdown between LEED and ES
is unclear.
Control sample is
buildings within 0.25
miles of certified
building.
No statistically significant sale price
premium for LEED. 19% sale price
premium for Energy Star.
15
No systematic treatment of data errors.
Find a positive relationship between
energy efficiency measure and level of
rental premium.
Potential omitted variable bias due to
dual certified buildings not taken into
account. LEED result is puzzling and
may be due to dual certification
problem.
Fuerst and
McAllister (2011)
Asking rents for 990 ES and 210
LEED certified buildings.
Published in Real
Estate Economics
Sale prices for 662 ES and 139
LEED certified buildings 19992009.
Chegut, Eichholtz,
Kok and Quigley
(2010)
Sale prices for 78 office BREEAM
offices in UK. Sales data obtained
from Real Capital Analytics
Draft working
paper
‘Achieved’ rents for 1011 offices
obtained from CoStar
Dermisi (2009)
Published in
Journal of
Sustainable Real
Estate
Fuerst and
McAllister (2009)
Published in
Journal of
Sustainable Real
Estate
Fisher and Pivo
(2009)
Draft working
paper
Hedonic OLS regressions
for rental and sales
prices.
Control sample is based
on buildings within same
CoStar submarkets.
Hedonic OLS regressions
and PSM for rental and
sales prices.
Location control is
buildings with 500m
radius.
6% rental premium for ES and LEED
certified buildings.
No systematic treatment of data errors.
Potential omitted variable bias issue due
to dual certified buildings.
35% and 31% price premium for LEED
and ES.
No systematic treatment of data errors.
Estimate rental premium of 16-20% to
eco-certification. Excellent – 22%-27%
Very Good – 18%-19%
Good – 8%-11%
Pass – 17%-18%
Controls for spatial auto-correlation.
In two out of three specifications – no
statistically significant sale price
premium
No age control reported in rental
regression. Included?
No discussion of controls for lease
terms, lease incentives unexpired lease
length, tenant quality, building quality.
Appraisal estimates of Market
Values for 351 LEED-rated US
office buildings. Study period is
first half of 2009.
Robust, MLE and fixed
effects regression
models.
Provides detailed results for NC, EB and
CS sub-samples. Results are inconsistent
between specifications. For LEED NC,
estimates a 36% discount for LEED
Silver. LEED EB has a 118% premium.
Small sample problems in the sales data?
Results do not seem plausible. It is not
clear what the benchmark variable is for
value differences - “other LEED
buildings”? Seem to be an absence of
location controls.
Occupancy rates for 292 LEED and
1,291 ES office buildings from a
sample taken in December 2008.
OLS and quantile
regression.
Estimate 8% occupancy premium for
LEED offices and 3% premium for ES.
Did not control for potential omitted
variable bias due to dual certification.
Quantile regressions find that, for ES
offices, increased occupancy is
concentrated in first two deciles only.
Investment performance of RPI
properties – 209 Energy Star, 158
in regeneration areas, 669 near
transit stations.
Hedonic OLS
procedures.
Estimate 12.5% premium on appraised
capital value for Energy Star compared
to buildings in same CBSA.
CBSAs are the location
controls.
Estimates 1% higher occupancy rates in
Energy Star buildings.
Estimate that utility costs were 10%
lower in Energy Star offices.
Energy Star offices have significantly
lower income and total returns.
Location controls are unlikely to fully
16
Deng, Li and
Quigley (2011)
Regional Science
and Urban
Economics
forthcoming
Residential sales prices of 74,278
dwelling units in 1439 projects in
Singapore between 2000-2010.
Hedonic OLS and GLS
procedures. Sample
refined using PSM.
4% of sample had Green Mark by
2009. Had 18269 transactions in
62 residential projects with Green
Mark were matched against 55982
transactions.
Location control is 55
planning areas. They
also use project fixed
effects in one model
specification.
Significantly different results for
different models
PSM Regression
Estimate average price premium for
Green Mark of about 4-6%.
Platinum – 14%
Gold Plus – 2.3%
Gold – 5.5%
Certified – 0.1%
51% Gold
21% Gold-plus
19% Green Mark
3% Platinum
Draft working
paper
A blend of residential asking prices
(80207) and actual (2063) sale
prices for Tokyo 2005-2009.
Estimate average price premium for
Green Mark of about 14-21%.
Platinum – 21%
Gold Plus – 15%
Gold – 15%
Certified – 10%
Estimate a 5% asking price premium for
Green Label.
Hedonic OLS
procedures.
Appears that approx 14%-15% of
the sample had eco-labels.
Brounen and Kok
(2009)
18,190 residential sale prices in the
Netherlands in 2008 for buildings
with EPC rating.
It is a concern that the premium for Gold
is higher than the Gold-Plus in the PSM
model.
Project Fixed Effects Regression
Have data on type of sale and
buyer type.
Shimizu (2010)
capture the effects of variations in
location quality.
No explanation is offered for the fairly
dramatic differences in estimated
premium for the two model
specifications.
Hedonic OLS procedures
Forthcoming in
Journal of
Environmental
Economics and
Management
For ‘better’ model estimate a 3.4%
premium for units rated A, B or C.
Compared to buildings rated G, they
estimate premiums of 12%, 7% and 4%
for A, B and C respectively.
No statistically significant effect
estimated for time on market.
17
Some puzzling results. Effect of energy
efficient equipment is significantly
negative. Premium is lower for better
environmental performance in some
variables e.g. insulation and long life
There are potential limitations due to
lack of controls for quality. Higher rated
buildings may be located in higher value
locations within urban areas and/or have
superior construction and/or
specification. For instance, the only
quality variable is condition and it is
notable that, when it is included in the
model, the premium drops substantially.
Fuerst and
McAllister (2010)
Ecological
Economics
Eichholtz, Kok and
Quigley (2010)
Working paper
Weighted Average rents for 1846
Energy Star, 268 LEED and 254
dual certified office buildings.
Sale prices for 876 ES, 87 LEED
and 123 dual certified office
buildings 1999-2009.
Occupancy rates for 2111 ES, 313
LEED and 254 dual certified office
buildings.
Two samples – 2009 and 2007
2007 694 green office buildings in
a total sample of 8182
2009 – 2687 observations in a
sample of 26794
Sales 744 rated 5249 control
Rents 1943 rated 18858 control
OLS and robust
regression techniques.
Estimate rental premium of 3%-4% for
ES. 4%-5% for LEED and 9%-10% for
dual certified.
Control sample is based
on buildings within same
CoStar submarkets.
Estimate sale price premium of 18% for
ES, 25% for LEED and 28%-29% for
dual certified buildings.
Estimate 1%-3% higher occupancy rate
for ES and 5%-6% lower occupancy rate
for LEED.
When comparing performance of 2007
sample, they find that estimated rental
premium goes down to 1.2%
Use panel data approach
to compare 2007 and
2009 samples.
WLS hedonic modelled
with propensity score
weighting of
observations
For 2009 sample, estimate rent premium
of 2% for ES and 6% for LEED.
Estimate sale price premium of 13% for
ES and 11% for LEED.
Breakdown between LEED and ES
is unclear. Mentions 209 LEED
rental buildings later in paper.
Attempt to control for the potential of
data errors and the inclusion of dual
certified buildings to produce biased
results.
No control for potential bias due to
spatial auto-correlation.
The sale price premiums seem high. Is
there an omitted variable problem?
Does not control for potential omitted
variable bias due to dual certification.
No apparent controls for outliers or data
errors.
Why is aggregate green premium higher
than LEED and ES? Why is ES sale
price premium higher than LEED – dual
certification problem?
Finds that LEED registration is
associated with rental premium of 7.9%!
Not clear how potential tendency for
LEED to have higher levels of single
occupancy is handled in effective rent
estimations.
Also analyse the effect of LEED scores
on rental premiums but not on sales
premiums.
The summary statistics contain some
puzzling numbers. Whilst the control
buildings have higher average rents and
sale prices than green labelled buildings,
the control buildings are on average
18
older, smaller and of lower quality (70%
Class B or C compared to 25% of
labelled buildings).
Yoshida and
Sugiura
Presented in March
2011
1154 buildings evaluated under
Tokyo Green Building Program
which is mandatory for buildings
exceeding 5000 square metres.
Use standard hedonic
regression procedure.
Find a statistically significant discount of
5.5% for labelled buildings. Discount is
robust in a number of tests.
When they estimate the price effects of
the different components of the rating,
they find mixed results. The individual
drivers of the negative premium tend to
be increased energy efficiency, water
efficiency and planting. They suggest
that perceived additional maintenance
costs associated with these features may
produce the discount.
They obtain a total sample of
34862 sales of condominiums. It is
not clear how many condominiums
are TGBP rated.
Jaffee, Stanton and
Wallace
(November 2010)
15230 transactions (2001-10) for
office buildings located in 43 US
metropolitan areas.
Working paper
545 Energy Star properties (3.6%
of total). Only 142 rated at the
time of sale (0.93% of total).
Zheng, Wu, Kahn
and Deng
Have data of total expenses (1473),
NOI (1532) and cap rates (2323)
for a subset of the sample.
(Presumably) Due to missing
variables, this sample decreases
substantially in the hedonic
models.
In the absence of an eco-label in
China, an index of building
Find evidence of lower depreciation for
eco-labelled condominiums.
Estimate an Energy Star premium of
13.5% when total expenses and operating
costs are excluded as confounding
factors.
When they are included in the model of a
subset of the data, the ES premium
becomes statistically insignificant.
Although sample size for Energy Star is
not stated, it is likely to be quite small.
Use standard hedonic
regression procedure.
In a subset of 816 observations, they find
no significant ES effect on NOI, cap rate
or operating expenses. However, again
sample size of ES is not indicated and
may be small.
Estimates a green premium of 9.1%.
Uses standard hedonic
procedure.
19
No control for potential bias due to
spatial auto-correlation.
The authors spend a great deal of effort
trying to falsify their finding of a
discount. However, their robustness
checks further confirmed their findings.
When presented, it was argued that the
finding was consistent with research in
the 1980s that found that buyers tended
to be sceptical of non-familiar
environmental technologies when
purchasing homes. Additional costs of
maintenance are emphasised.
The location control seems broad given
the fact that ES buildings tend to be
concentrated in CBDs.
If sample size of ES is small, weak
effects may be missed.
Not clear if authors have tested for
multi-collinearity. If there is a negative
association between operating expenses
and ES, it is possible that ES coefficient
may be biased.
It is not clear how the premium is
estimated. The result implies that
Feb 2011
Fuerst and
McAllister
Energy Policy,
published 2011
greenness is estimated from
marketing claims. Apply this
index to 1992 residential building
complexes constructed 2003-2008.
Examines effect of EPC rating on
yield, Market Value and Market
Rent for 708 commercial property
assets in IPD UK. Includes 23
BREEAM rated buildings. Date of
data is Q3 2010.
buildings were rated in a binary manner.
However, the paper describes the
creation of a relative score.
Uses standard hedonic
procedure.
Finds no significant effect of EPC rating
on Market Rent and Market Value.
Very similar results for yield estimation.
For one EPC rating (E compared to G for
retail) was the coefficient significantly
negative at the 10% level.
Given the large geographical scope
of the sample and the number of
EPC and geographical ‘segments’,
it is likely that there were small
sample effects.
Kok and Jennen
May 2011
Examines the effect of EPC rating
and Energy Index on 1072 rental
transactions in Netherlands for the
period 2005-2010.
It is not clear how or whether separate
groups of green buildings were
identified. If they were identified, it is
not clear how many there were.
Paper argues that a larger sample is
needed to provide a robust estimation of
whether weak effects are being missed.
The BREEAM results were similar. The
coefficient on the BREEAM dummy was
statistically significantly negative at the
10% level.
Uses standard hedonic
procedure.
Finds a rental premium of approximately
7% for buildings rated C or lower
compared to buildings rated D and
above. Compared to D rated buildings,
find significant premiums of about 10%
for C rated and 5% for B rated. No
significant discount for E, F and G rated
buildings compared to D rated.
Working paper.
There may be a potential omitted
variable problem. Buildings rated Class
A, B and C may be better quality than
buildings with inferior performance.
The level of energy efficiency may be
correlated with other unobserved quality
variables e.g. design, interior finishes etc
The pattern of premiums seems odd.
Premiums decline from C to A rather
than increase.
Surprisingly, age of building does not
have a statistically significant effect on
rent.
The sample of transactions covers the
period 2005-2010. When were EPCs
20
introduced in Netherlands? For the UK,
it was 2009.
Harrison and Seiler
Published in
Journal of
Property
Investment and
Finance 2011
Investigates whether there are
rental price and occupancy rate
premia in LEED and Energy Star
certified office buildings.
Also looks at variations in nature
of variations focussing on the
hypothesis that political
characteristics determine level of
premium.
Uses hedonic procedure
to investigate rent
determinants. Seem to
omit a number of
variables – e.g. size, land
area, stories.
Does not report on price effects of ecolabel per se. However, the beta for the
non-certified variable (0.93) implies a
rental premium of 6%-7%.
Estimate that premiums are higher in
Democrat voting areas.
No information is provided on
number of assets in sample nor
number of eco-certified assets.
Newell, McFarlane
and Kok
Report for
Australian
Property Institute
September 2011
Compares 206 NABERS rated
office buildings with 160 nonNABERS rated buildings in
Sydney and Canberra in March
2011. 23 (4-6) Green Star rated
buildings were also included.
Covers 51% of the total office
market.
Uses hedonic procedure
to investigate rent and
value (“a constructed
proxy which incorporates
gross rent, vacancy,
incentives, outgoings and
yields”) determinants.
Seem to omit a number
of potential price
determinants – e.g.
stories, age, unexpired
lease terms, tenant
quality etc
21
Providing no indication of statistical
significance, the study finds a small
rental and value premium for high
NABERS-rated buildings 0.3% and 1.9%
specifically. Finds a discount in value
for offices with NABERS rating of 2.5
stars or less.
In the UK, buildings may have multiple
EPCs. This issue is not discussed.
Much more detail required on sample
and modelling to evaluate this paper.
Using rents/vacancy rates of noncertified assets as independent variables
in regression model produces high
explanatory power but introduces issues
of endogeneity and multi-collinearity
which are not discussed in the paper.
The limited number of control variables
may also be a problem. For instance,
since there is a well-known positive
correlation between educational
attainment and Democratic voting, there
may be an omitted variable bias.
The limited number of control variables
may be a problem.
The lack of any reporting of tests for
statistical significance is a major
omission.
Appendix 2 - Relevant Publications by the Research Team
Bruhns, H. and Wyatt, P. (2011) A data framework for measuring the energy consumption of the nondomestic building stock, Building Research & Information, 39, 3, 211-226
Caijas, M., Fuerst, F., McAllister, P and Nanda, A. Do Responsible Real Estate Companies
Outperform Their Peers? under review in Real Estate Economics.
Clapp, J., Nanda, A., and Ross, S. (2008). Which School Attributes Matter? The Influence of School
District Performance and Demographic Composition on Property Values, Journal of Urban
Economics, 63, 2, 451-466.
Fuerst, F., Kontokosta, C. and McAllister, P. Spatial Determinants of Green Building Adoption, under
review in Ecological Economics.
Fuerst, F. and McAllister, P. (2011) The Impact of Energy Performance Certificates on the Rental and
Capital Values of Commercial Property Assets: Some Preliminary Evidence from the UK, Energy
Policy, 39, 6608-6614.
Fuerst, F. and McAllister, P. (2011) Eco-labelling in Commercial Real Office Markets: Do LEED and
Energy Star Offices Obtain Multiple Premiums?, Ecological Economics, 70, 6, 1220-1230.
Fuerst, F., McAllister, P., Van de Wetering, J. and Wyatt, P. (2011) Measuring the Financial
Performance of Green Buildings in the UK Commercial Property Market: Addressing the Data
Issues, Journal of Financial Management in Property and Construction, 16, 2, 163-185.
Fuerst, F. and McAllister, P. (2011) Green Noise or Green Value: Measuring the Price Effects of
Environmental Certification in Commercial Buildings, Real Estate Economics, 39, 1, 46-69.
Fuerst, F.; Reichardt, A.; Rottke, N.; Zietz, J. (2012): Sustainable Building Certification and the Rent
Premium: A Panel Data Approach. Journal of Real Estate Research. Published online.
Fuerst, F. and McAllister, P. (2009) An Investigation of the Effects of Eco-Labelling on Office
Occupancy Rates, Journal of Sustainable Real Estate, 1, 1, 49-64.
Fuerst, F. (2009): Building Momentum: An Analysis of Investment Trends in LEED and Energy-StarCertified Properties. Journal of Retail and Leisure Property, Vol. 8/4, 285-297.
McAllister, P. (2009) Assessing the valuation implications of the eco-labelling of commercial
property assets, Journal of Retail and Leisure Property, 8, 4, 311-322.
McAllister, P. Editor of Special Edition (2009) Sustainable Property: Building the Research Base,
Journal of Retail and Leisure Property, 8, 9, 239-322.
van de Wetering, J. and Wyatt, P. (2010) Measuring the carbon footprint of existing office space,
Journal of Property Research, 27, 4, 309-336
van de Wetering, J. and Wyatt, P. (2011) Office sustainability: occupier perceptions and
implementation of policy, Journal of European Real Estate Research, 4, 1, 29-47
Dr Wyatt has also contributed substantially to the National Energy Efficiency Data-Framework:
Report on the development of a data-framework and initial analysis, June 2011, Department of
Energy & Climate Change
22