Factors affecting homebuyers' willingness to pay green building price premium: Evidence from a nationwide survey in Israel

Accepted Manuscript Factors affecting homebuyers' willingness to pay green building price premium: Evidence from a nationwide survey in Israel Boris A. Portnov, Tammy Trop, Alina Svechkina, Shoshi Ofek, Sagi Akron, Andrea Ghermandi PII:

S0360-1323(18)30218-X

DOI:

10.1016/j.buildenv.2018.04.014

Reference:

BAE 5410

To appear in:

Building and Environment

Received Date: 22 February 2018 Revised Date:

7 April 2018

Accepted Date: 9 April 2018

Please cite this article as: Portnov BA, Trop T, Svechkina A, Ofek S, Akron S, Ghermandi A, Factors affecting homebuyers' willingness to pay green building price premium: Evidence from a nationwide survey in Israel, Building and Environment (2018), doi: 10.1016/j.buildenv.2018.04.014. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Factors affecting homebuyers' willingness to pay green building price premium: Evidence from a nationwide survey in Israel Boris A. Portnov, Tammy Trop, Alina Svechkina, Shoshi Ofek, Sagi Akron and Andrea Ghermandi

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Faculty of Management, University of Haifa, Mt. Carmel, Haifa, Israel 3498838

Abstract

Green buildings (GBs) bring multiple benefits to homebuyers. However, the lack of knowledge or uncertainty about these benefits, combined with a nominal price

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premium (PP) for GBs, may prevent prospective homebuyers from entering the GB market. Therefore, governmental incentives may be needed. The present study serves

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the dual purpose of examining the PP size that prospective homebuyers in Israel are willing to pay (WTP) for GBs, and investigating, for the first time, the potential impact of prevalent GB policy instruments on the premium’s size. Findings from a nationwide online survey indicate an acceptable PP in the range of 7–10%. Expected maintenance savings and familiarity with GB concept and benefits are found to be positively associated with the size of the premium, while counterintuitively, financial

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incentives, such as tax breaks and subsidized loans, are found to result in lesser, rather than greater, WTP PP. This indicates that financial incentives to homebuyers may be counterproductive by generating emotive and opposite responses, and that a long-term governmental commitment to support GB maintenance may be more effective. The

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study suggests a mix of financial and non-financial GB incentives to homebuyers. The study mainly contributes to better-understanding of how potential homebuyers’ GB

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choice can be encouraged by applying informed policy tools. It also emphasizes the importance of evaluating unexpected consequences of future interventions in the GB market.

Keywords: Green buildings (GBs); public incentives; price premium

1. Introduction Green, or environmentally friendly, buildings are rapidly becoming a national priority in many countries worldwide [1-3], and various incentive schemes have been designed to promote their adoption [4-5]. Yet, the uptake of green buildings (GBs) is still moderate. 1

ACCEPTED MANUSCRIPT Although there are various definitions and rating systems for GBs around the world, it is generally accepted that GB is the practice of creating and using more resourceefficient

models

of

planning,

design,

construction,

renovation,

operation,

maintenance, and demolition of buildings [6-9]. It is also commonly acknowledged that GB brings together a vast array of practices and techniques to minimize the

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negative impacts of buildings on resource consumption, the environment, and human health [9,10].

The GB concept first emerged in the 1970's, when, in an attempt to incorporate sustainable development principles, the construction industry began to design and

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construct GBs [11,12]. Since then, popularity of GBs has continued to increase with the help of globally regulated building-rating systems, such the Leadership in Energy

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and Environmental Design (LEED) in the USA, BREEAM (Building Research Establishment Environmental Assessment Method) in the UK and EU countries, the Green Star system in Australia and New Zeeland, and CASBEE in Japan [13-14]. As Doan et al. [10] point out, although these four rating systems were initiated in different contexts with different standards, indoor environment quality, energy, and material are core common categories for all.

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In Israel, the GB Standard (IS 5281) was first established in 2005 and has been upgraded and adapted to international standards in 2011 and again in 2016 [15]. While the original standard referred only to residential and office buildings, its revised version defines seven different standards for various types of buildings -

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residential, offices, healthcare institutions, public, commercial, education, and tourism - and refers to both new construction and retrofitting of existing buildings. The

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standard does not provide a definition of GB, yet it encompasses all the common core categories of the leading GB rating systems, including efficient use of energy, land, water, and building materials; improvement of indoor environmental quality; reduction of waste; and minimization of negative impacts on the environmental. During the past decade, the growth rate of GB has been rapid. Based on a survey conducted in 69 countries, it is estimated that global GB doubles, and will continue to double, every three years [16]. A similar trend is observed in Israel, where the number of certified buildings has increased from 180 in 2014 to 395 in 2017, and another 650 buildings are currently undergoing GB certification [15]. The GB adoption trend is

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ACCEPTED MANUSCRIPT sustained by energy conservation needs and growing public awareness about the substantial ecological footprint of the build environment [17]. Previous studies suggest that GBs have considerable benefits to users and developers alike: users may benefit from improved health and productivity, reduced consumption of energy and water, lower maintenance and operational costs, and better indoor

corporate image and increased future competitiveness [25].

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environmental quality [18-24]), while developers may benefit from improved

Despite the reported benefits, common barriers to the widespread adoption of GBs still exist, mainly because GBs often cost more to developers, pose extra risks, and

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require more time to deliver, compared to similar conventional buildings. These additional costs are eventually transferred to homebuyers [9,24,26-29].

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The cost and price premium (PP) of GBs vary according to their rating and other factors. Empirical studies suggest that GBs accrue 1–12.5% additional costs to developers [20,30-32], and carry an additional PP of 10–31% to homebuyers [9,3335].

Another barrier is the lack of sufficient considerations about post-occupancy

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maintenance of GBs, which often results in lifelong resource inefficiencies and emissions [36]. This is a critical problem, since most economic benefits to GBs’ homebuyers can materialize only through proper maintenance and long-term energy savings [36-39, 41].

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Due to additional upfront costs and lack of knowledge or uncertainty about potential benefits, many developers and homeowners may avoid entering the GB market, and

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governmental incentives may be needed to encourage them [9,42-43]. Currently, in Israel there are no governmental financial or non-financial incentives for GBs, and the existing barriers for their adoption have not yet been properly studied and sufficiently understood [44]. Only recently, the Center for Research and Information of the Israeli Parliament [45] pointed out that the most significant barriers to GB adoption are low awareness of contractors, consumers and planning institutions about costs and benefits of compliance with the GB standard; and concerns that promoting GB would further increase real estate prices. Recent literature on GBs covers a wide range of topics, such as benefits and costs of GBs [9,17,23,29], GB rating systems [10,46-47], barriers to GB adoption [48-50], 3

ACCEPTED MANUSCRIPT homebuyers’ and developers’ motivations to adopt GBs [51-52], energy-conscious design of building attributes [11-12,53], land-use efficiency [54-57], reduction in energy and water outlays through proper maintenance [31,36,58-59], occupant behavior associated with water- and energy-savings [60-62], and GB occupant satisfaction with indoor environmental quality [63,64]. However, research on the

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factors affecting GB choice by consumers is still rather limited and fragmented [9,43,68-70]. In particular, the effect of various public policy incentives on consumers’ demand and willingness to pay (WTP) a PP for GBs, has not yet been examined in sufficient depth by researchers, or well understood by stakeholders.

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The present study contributes to this new line of research, by investigating the PP that prospective homebuyers in Israel are willing to pay for GBs, and the potential effect

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of different public incentives on the size of this premium. The study's findings provide new insights into the efficacy of common incentives to homebuyers to act as drivers for GB choice.

2. Factors influencing GBs adoption

There are many underlying aspects that still dominate developers’ and consumers’

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considerations with regards to GB adoption, especially in the residential sector (e.g., [51-52,71]). General motivations behind GB adoption are discussed in the following sub-sections.

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2.1 Motivational drivers for GB

Pro-environmental motivations and expectations, which may be behind growing public interest in GBs, are captured by a number of behavioral price theories,

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including the Theory of Planned Behavior (TPB) [72], Social Marketing Model [73], Value Belief Norms Theory [74], and Social Dilemma System Model [75]. According to Salazar et al. [76], WTP for an environmentally friendly product is a unique attribute of these theories that place an emphasis on behavioral intentions, values, and norms. In this conceptual framework, behavioral motivations can be seen as an inducement by internal or external factors to stimulate desire and energy in individuals to be interested and committed to perform a given behavior [71,77-80]. With regard to GB, motivational drivers for developers and homebuyers are likely to be different.

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ACCEPTED MANUSCRIPT Motivational drivers of developers Several GB studies show, that the most important environmental drivers behind GB developers’ motivation include pro-environmental altruism [52], energy conservation [51,83], indoor environmental quality improvement [51-52], water and air quality improvement [51], environmental/resource conservation [51-52,83], and waste

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reduction [51,83]. Olanipekun et al. [52] point out additional drivers, such as enhanced reputation, persuasive influence, market appeal, and financial and nonfinancial governmental incentives. The latter researchers did not find significant differences in motivation drivers between public and private developers for engaging

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in GB projects.

Nevertheless, there are several underlying reasons that may prevent or inhibit the GB

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market from fully realizing its benefits [48,71,77,81-82]. In general, GB requires comparably higher overall costs for the developer. These costs are influenced by the asset specificity, i.e., the durable investment to support a particular transaction, which may include, for instance, extra knowledge, skills, equipment, work, facilities, and administrative burden in the process of planning and implementing GB initiatives [8385]. Extra risks to deliver may also impact the total project cost. Such additional risks

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can include market, policy, and economic uncertainties regarding GB assessment and building techniques [86].

Motivational drivers of homebuyers

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From the homebuyers’ point of view, there are three general barriers that may reduce their WTP a PP for GBs. These include long payback time, agency problems, and

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visibility [82]. If the payback time is longer than two years, consumers are generally impervious to upfront investments, even if these are not very large [48]. Agency problems refer to ineffectiveness of the incentives, while visibility relates to the fact that consumers often do not see the breakdown of costs of their own energy and water usage for heating, cooling and other uses. The lack of distinguishable relationship between usage and costs also affects individual behavior. As Gan et al. [77] suggest, consumers' environmental concerns are only weakly linked to purchasing decision of green products, whereas awareness about green products is much more influential. Thus, an individual who is aware of the availability of green brands, would have a stronger preference to buy them.

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ACCEPTED MANUSCRIPT Hu et al. [71] investigated the effect of different environmental and sociodemographic factors on homebuyers' WTP GB premium in the city of Nanjing, China. The study showed that house price, environmental pollution, and transport and job accessibility were more important considerations in home purchasing decisions than GB features and attributes of apartments and houses. This conclusion echoes the

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results of several other studies [87-89], which indicated that GBs are sufficiently attractive only if they are located in good neighborhoods with clean air and good job accessibility. Burnett et al. [20] also suggest that buyers’ considerations are still

2.2 Size of GB premium

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dominated by location and affordability, as well as culture and individual preference.

Cost premium and PP are two different types of outlays associated with GBs, and may

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differ in size, as detailed in the subsections below. Cost premium

As residential buildings differ by location, size, quality of construction materials, and GB rating, additional costs for incorporating GB features differ accordingly. Kats et al. [18] and Kats [31] estimated 0.66% extra cost for basic LEED certification, 1.82–

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2.11% for Silver and Gold, and 6.50% for Platinum LEED certification in the US. Ellis and Hadley [30] provide similar estimates, placing GB cost premium between 2% and 3% for the basic level of certification, 5–7.5% for higher certification levels, and up to 12.5% for zero-carbon buildings. Langdon [27]

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estimates 3–5% greater costs for five stars GBs and 6% for six stars GBs in Australia, where the Green Star rating system is used.

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In Hong Kong, under the local Building Energy Assessment Methods (BEAM Plus), the cost premiums are estimated as 0.8%, 1.3%, and 3.2%, for Silver, Gold and Platinum buildings, respectively [20]. Yu and Tu [32] calculated that GBs require 1– 3% extra cost compared with non-GBs in Singapore. Price premium (PP) GB PP for homebuyers is likely to be higher than developers’ cost premium, mainly due to developers’ profit and extra risks to deliver [86]. Fuerst and McAllister [33] put GB PP in the range of 10–31% for buildings certified in the UK by Energy Star and LEED, respectively. The World Business Council on Sustainable Development (2007) reported a similar range of 11–28% in GB PP. Miller et al. [90] came up with 6

ACCEPTED MANUSCRIPT slightly lower PP estimates of 9.94% for LEED and 5.76% for Energy Star certified buildings in USA. 2.3 GB incentives to homebuyers Due to multiple barriers inhibiting the GB market from fully realizing its benefits, governmental intervention may be required to facilitate the housing market transition

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from conventional to GBs [9,91-95].

According to OECD [4], the government’s role in motivating GB homebuyers can be instrumental by reducing taxes, providing subsidies, funding communication campaigns, and providing GB education for both consumers and developers. The role

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of governmental and public agencies can also be important in protecting consumers

reporting.

3. Research method

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from misleading information through labelling, advertising, and faulty corporate

An internet survey was carried out to determine the size of PP that prospective homebuyers in Israel are willing to pay for GBs, and to investigate the effect of different factors, including various GB incentives, on the size of this premium.

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3.1 Survey population

The nationwide survey was carried out among 438 potential homebuyers in AprilMay 2016, using an internet panel technology. The sample size was calculated to

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represent the general population of the country, with ±4.5% margin of error in respect to gender, age (within the 17–70 age group), educational level, and region of

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residence. The survey participants were randomly drawn from the survey company’s database to represent the required socio-demographic and regional characteristics. In addition, to control for the relevancy of WTP GB premium, the main screening criterion for inclusion in the sample was the respondent's intention to buy an apartment or a house in the next five years. The post-survey assessments, reported in Table 1, demonstrates that even after screening, the sample was fairly representative of the country’s population in terms of the above-mentioned socio-demographic and locational criteria. The survey was carried out by Dialog Ltd, a company specializing in population surveys and survey consulting.

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ACCEPTED MANUSCRIPT 3.2 Survey questionnaire The survey questionnaire consisted of four parts (Appendix A) and was approved by the University of Haifa Ethics Committee (Approval No. 163/16). The first part aimed at determining potential homebuyers' familiarity with the GB concept and awareness of GB benefits. The respondents were asked to rate their degree of agreement with, or

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endorsement of, given statements, such as “GBs can reduce electricity bills” or “GBs can reduce water consumption”. The respondents were asked to use a 10-point Likert scale, ranging from "disagree" (1) to "strongly agree" (10). The respondents were also offered an “I don't know” option. The statements offered for endorsement were

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designed to reflect the main requirements for GB certification, such as reduction in water and energy consumption, reduction in construction and demolition waste, etc.

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[62,83,96].

The second part of the questionnaire explored the effectiveness of various prevalent GB incentives in motivating potential homebuyers to purchase a GB. The respondents were asked to indicate their degree of agreement with, or endorsement of, statements regarding each incentive, using a 10-point Likert scale, ranging from "disagree" (1) to "strongly agree" (10). The list of GB incentives offered for evaluation was based on

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OECD's findings [15,45], which suggested that the following policy measures can potentially be effective as motivational drivers: a) improved information sharing about GB projects and benefits;

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b) guided tours to GB sites;

c) organized GB courses and workshops; d) subsidized loans and grants for GB apartment buyers;

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e) mandatory building labeling according to GB rating;

f) tax reductions for GB buyers; and g) education about GB benefits.

The third part of the questionnaire was designed to examine the size of PP which prospective homebuyers are willing to pay for a GB apartment or house compared to a conventional apartment or house of the same size and location. The respondents were asked to state their preferences choosing from a continuous premium scale, ranging from zero (i.e., refusal to pay any extra price) to 30%.

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ACCEPTED MANUSCRIPT Previous studies have explored residents’ stated WTP for green building attributes through additional management fees [97], and have observed how stated WTP may differ from actual WTP, as revealed through housing markets analysis [98]. Following recommendations to reduce survey biases and overestimation of stated WTP, Zalejska-Jonsson’s [98] questionnaire stated the GB premium criterion of value used -

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by anchoring it to the price of a conventional apartment - and was explicit about how the results would be used and might influence policies or strategies. Moreover, the chosen payment assessment mechanism (i.e., payment scale) has been shown to lead to a smaller stated WTP than a dichotomous choice (or referendum) format [99] and

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be less open to strategic overstatement than open-ended questions [100]. Finally, the questionnaire included a series of questions aimed at eliciting the respondents' level of

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familiarity and knowledge of GBs, which may affect their stated WTP [98]. The fourth part of the questionnaire contained questions designed to help determine what socio-demographic factors may influence respondents’ opinions and answers. Selected characteristics of the study population are presented in Table 1. Table 1. Selected characteristics of survey participants

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Variable

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Gender Male Female Number of years of schooling Age Monthly income (NIS; NIS1≈0.28US$) up to 12000 12,000-18,000 18,000-24,000 24,000 and more Average income (NIS): District of residence: North Center Southb General familiarity with GB concept a

Study cohort SD Mean or (%)a

Population of Israel (as of 2016, unless stated otherwise)c

(48.5) (51.5) 16.74 38.41

2.21 11.64

(49.6) (50.4) 16.00e 39.8d

(76.0) (17.4) (4.8) (1.8) 10,618f

-

11,219g

(29.5) (43.1) (27.4) (7.14)

2.46

(27.9) (41.1) (31.0)

Number of respondents=438 (100%); b Includes Southern and Jerusalem districts; c Source: ICBS (2017) unless stated otherwise; d Calculated using [101] data for the 17-70 age group; e Source: UNDP (2016) Human development report (http://hdr.undp.org/sites/all/themes/hdr_theme/countrynotes/ISR.pdf): f authors’ estimate; g Average net income of a male employee (source: [101]).

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ACCEPTED MANUSCRIPT 3.3 Statistical analysis Survey results were analyzed in two stages. First, descriptive statistics were calculated to assess the degree of familiarity with GB concept and potential benefits. Next, a multiple regression analysis was applied to determine whether familiarity with the GB

that potential homebuyers are willing to pay.

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concept and different GB incentives is significantly associated with the size of the PP

The dependent variable in the analysis was the size of the PP as percent of the total purchase costs, while explanatory variables were various policy measures aimed at motivating GB purchase decisions, familiarity with the GB concept, and individual

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attributes, such as age, educational level, and region of residence. As some explanatory variables were strongly collinear, the principal component analysis was

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implemented to extract “orthogonal” (i.e., uncorrelated) components for a subsequent regression analysis (see Subsections 4.5-4.6).

4. Results

4.1 Familiarity with the GB concept and benefits

Fig. 1 presents the level of importance, on a 10-point scale, assigned to various

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aspects of GBs by the survey participants. The results show that all GB aspects offered for evaluation were ranked relatively high, ranging between 7.21±0.11

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(healthier living environment) to 8.22±0.09 (water use reduction).

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ACCEPTED MANUSCRIPT Fig. 1. Familiarity of survey participants with various aspects of GBs (average number of points on a 10-point scale; N=438) (No color) 4.2 Acceptable size of GB PP Table 2 presents the acceptable PP, calculated separately for subgroups of potential

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consumers who have different levels of familiarity with the GB concept. As Table 2 shows, the size of the premium varies, quite expectedly, according to the level of familiarity with the GB concept. In particular, potential homebuyers who reported relatively poor familiarity with this concept (less than 5 on a 10-point scale), were

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ready to pay a lower PP of 7.74±0.74%, while those who are more familiar with the concept were ready to pay a higher PP of 9.25±0.35%. The difference between these

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two groups of prospective homebuyers thus reaches 1.51% and is statistically significant, as indicated by a F-test for cross-group differences (F=4.134; P<0.05). In absolute terms, this percent difference amounts to about USD 6,500, relative to the average price of an average newly-built housing unit in Israel, which is about USD 425,000 at present [101].1 Characteristically, 18.3% of the respondents were unwilling to pay GB PP at all, with such percentage being higher among those who were poorly

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familiar with the GB concept (33.3%) and lower among those who acknowledged better familiarity with it (12.7%).

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Table 2. Acceptable size of GB price premium across survey participants with different levels of familiarity with the GB concept (Method – ANOVA) Na

Mean (%)

SE

F

Sig.

Low (1 to 5)

102

7.735

0.740

4.135

0.043

323

9.254

0.349

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Familiarity with the GB concept (on a 10-point scale) Medium-high (6 to 10) a

Excludes cases with missing data

4.3 Ranking of GB incentives Fig. 2 reports average scores, on a 10-point scale, assigned by survey respondents to various policy measures. The findings show that economic incentives are ranked at the top of the policy measures list, with 9.04±0.74 points to tax reduction, and 1

According to ICBS (2017) [101], housing prices in Israel vary widely across regions, being more expensive in the central, densely populated part of the country, by about 80% than in its peripheral regions. In 2016, the average gross internal floor area of a newly built housing unit in Israel was 185 m2 and its average number of rooms was 4.8.

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ACCEPTED MANUSCRIPT 9.00±0.75 points to subsidized loans and grants, indicating that these factors are perceived as main stimuli for purchasing GB. In comparison, guided tours to GB sites, and educational courses and workshops, are scored relatively lower, with 7.25±1.2 and 6.51±1.3 points, respectively.

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Tax reduction

9.04

Subsidized loans and grants

9.00

7.83

More information on GB benefits

7.79

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Education and advertisement

Building labeling by GB compliance

7.58

Guided tours to GB sites Courses and workshops

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7.25

6.51

6.00

7.00

8.00

9.00

Fig. 2. Ranking of public policy incentives by survey participants (average number of points on a 10-point scale; N=438)

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(No color)

4.4 Regression analysis (Phase 1)

Table 3 reports the results of the initial multiple regression analysis, linking the size

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of acceptable GB premium with various GB incentives and individual attributes of the study participants. The table reports two models - Model 1 with GB incentives only,

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and Model 2 with respondents’ educational attributes and region of residence added. It should be noted, that in the initial stages of the analysis, additional sociodemographic variables, such as income, ethnicity, and religious affiliation, were considered, but none of them emerged as statistically significant.

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ACCEPTED MANUSCRIPT Table 3. Factors affecting the size of GB price premium [Method – ordinary least square (OLS) regression; dependent variable – acceptable size of GB price premium] Variable

Model 1 B

t

B

tb

10.063 0.205 0.288

4.851** 1.454 7.422**

8.176 0.226 0.285

2.497** 1.592 7.316**

0.056 -0.023 0.027 -0.507 0.035 -0.466 0.137

0.235 -0.103 0.152 -1.289 0.163 -1.189 0.575

-

-

1.807 2.007 0.815

0.627 0.694 0.285

438 0.160 0.140 6.211

-

1.179 1.056 438 0.175 0.144 6.197

1.63 1.078

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unstandardized regression coefficient; b t-statistic and its significance level.** Indicate a 0.01 significance level (two-tailed); Model 1: Incentives only; Model 2: Education and regional variables added.

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a

a

0.021 0.134 0.351 -1.340 0.034 -1.179 0.667

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0.005 0.029 0.062 -0.524 0.007 -0.460 0.159

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(Constant) Familiarity with GB concept (10-point scale) Expected maintenance savings (%) Incentives (10-point scale): • More information on GB benefits (I1) • Guided tours to GB sites (I2) • GB courses and workshops (I3) • Subsidized loans and grants (I4) • Labeling of buildings according to GB rating (I5) • Tax reductions (I6) • Educating and advertising of GB advantages (I7) Education level: • High school • Professional • Academic Region of residence (reference category: Center): • North • South Number of cases R2 R2-adjusted Std. error of the estimate

Model 2 b

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a

In Model 1, only the anticipated size of future maintenance savings emerged as a

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statistically significant predictor of the acceptable PP (b=0.288, t=7.42; P<0.01). This result does not change when the models are controlled for respondents’ educational level and region of residence (Model 2). One possible explanation for this result is the existence of a strong collinearity between individual GB incentives (Appendix B), which can affect regression estimates and their statistical significance. To verify this assumption, uncorrelated components, known as "factors", were extracted, and the regression analysis was repeated, as detailed in the next subsections. 4.5 Factor analysis 13

ACCEPTED MANUSCRIPT Factor analysis is a method of data reduction that seeks to determine the underlying unobservable (i.e., latent) variables reflected in the observed variables, also known as the "manifest" or "principal component" variables [102]. The factor analysis can identify uncorrelated (or orthogonal) factors, which can then be used in multiple regression analysis as uncorrelated predictors.

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Table 4 presents the factor analysis results of GB incentives, which may potentially influence consumers’ WTP higher PPs. The analysis identified two factors as principal components underlying all the policy measures included in the analysis. The first factor (F1) is strongly and positively correlated with information sharing, guided

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tours to GB sites, course and workshops, building labeling and advertising of GB benefits (r=0742÷0.849). The second factor (F2) has strong correlations with

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subsidized loans and grants, and tax reductions (r=0.921÷0.928), but relatively weak correlations with the other incentives. Thus, F1 can conditionally be labeled as “information and education provision”, while F2 can be termed “economic incentives.” Both factors capture about 77% of the original variables’ variation, with F1 explaining some 47% of the variation.

Table 4. Factor analysis of policy measures affecting the size of GB price premium Rotated Component Matrix

F1

F2

F1

F2

Improved information sharing Guided tours to GB sites GB courses and workshops Subsidized loans and grants Labeling buildings according to GB rating Tax reductions More information about GB benefits

0.815 0.829 0.737 0.725 0.796 0.698 0.828

-0.139 -0.284 -0.421 0.625 -0.140 0.651 -0.167

0.758 0.849 0.847 0.263 0.742 0.226 0.783

0.331 0.218 0.053 0.921 0.320 0.928 0.315

Initial Eigenvalues % of Variance

4.226 60.374

1.139 16.279

Cumulative % Rotation Sums of Squared Loadings Total % of Variance

60.374

76.652 3.295 47.07

2.071 29.58

47.07

76.65

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Component Matrix

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Policy measure

Cumulative %

Component Score Coefficient Matrix F1 F2 0.228 0.301 0.349 -0.158 0.225 -0.176 0.244

0.004 -0.100 -0.213 0.553 0.001 0.568 -0.015

Notes: Extraction method: principal component analysis; rotation method: Varimax with Kaiser normalization.

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ACCEPTED MANUSCRIPT 4.6 Regression analysis (Phase 2) After the principal components have been identified, the regression analysis was performed again, using these components and several other individual-level variables as predictors. In the first phase, only functional variables (i.e., the extracted factors, expected percentage of monthly maintenance savings, and familiarity with the general

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concept of GB) were introduced into the model as predictors for acceptable GB PP. Next, several additional variables, such as age, number of schooling years, number of children, and religious affiliation were added. Lastly, a forward stepwise regression was performed to determine only the most significant factors affecting the size of the

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PP. Table 5 reports the re-estimated models: Model 3 contains functional factors only (F1, F2, maintenance savings, and declared familiarity with the GB concept), Model 4

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adds socio-demographic attributes of the survey participants (family size, religious affiliation, etc.), and Model 5 reports only statistically significant factors influencing the size of GB PP.

As Table 5 shows, the following two variables are significantly associated with the acceptable size of GB PP: the expected maintenance savings (b=0.298; t=7.89; P<0.01; Model 5) and F2 - Economic incentives (b=-1.12; t=-3.48; P<0.01). The

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regression coefficient of the former variable varies slightly across the models: from 0.289 in Model 3, to 0.294 in Model 4, and to 0.298 in Model 5, indicating that an expected increase in monthly maintenance savings by 1% is associated with WTP an additional 0.3% for GB. Characteristically, the latter variable (F2, economic

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incentives) emerged in the model with a negative sign (b=-1.12; t=-3.48; P<0.01). This implies that stronger economic incentives are likely to lead homebuyers, all other

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things held equal, to be willing to pay a smaller, rather than larger, PP.

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ACCEPTED MANUSCRIPT

Table 5. Factors affecting price premium (%) that potential homebuyers are willing to pay for GB (Method: multivariate regression analysis)

438 0.159 0.151 6.174 10.068

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0.980 0.960 0.923 0.902 -

6.198 0.294 0.026 -1.304 0.232 0.004 -0.005 -0.247 -0.479 -0.672 438 0.191 0.172 6.097 9.856

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2.652** 7.557** 0.282 -3.862** 1.544 -

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3.093 0.289 0.093 -1.294 0.211 -

Tolerancec

3.559** 7.632** 0.080 -3.884** 1.707 0.007 -1.534 -1.793* -2.782** -1.002

0.946 0.938 0.896 0.888 0.918 0.945 0.937 0.948 0.902

Model 5 (Stepwise - significant factors only) Tolerancec Ba tb 5.273 0.298 -1.117 -0.461 438 0.170 0.163 6.128 26.036

7.422** 7.892** -3.484** -2.731* -

0.995 0.993 0.996 -

unstandardized regression coefficient; b t-statistic and its significance level; c multi-collinearity diagnostic; * Indicates a 0.05 significance level (two-tailed); **indicates a 0.01 significance level (two-tailed).

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a

tb

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(Constant) Maintenance savings (% annual) Factor 1 (Information & Promotion) Factor 2 (Economic benefits) Familiarity with GB concept Gender (Male=0, Female=1) Age (years) Number of schooling years Number of children Religious affiliation (Non-religious=1, other=0) Number of cases R Square Adjusted R Square Std. error of the estimate F

Ba

Model 4 (Socio-demographic attributes added) Tolerancec Ba tb

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Model 3 (Functional factors only)

Variable

16

ACCEPTED MANUSCRIPT 4.7 Scenario testing In order to evaluate the consequences of future policy interventions in the GB market, the calculated factor component coefficient matrix (Table 4) and Model 5 (containing only the most significant determinants of PP) were subsequently used to evaluate different development scenarios, aimed at increasing homebuyers’ WTP a higher GB

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PP. The following three policy intervention packages were evaluated:

a) No policy intervention: the values of all relevant policy variables were set to their minimum (i.e., 1 point on a 10-point scale), while the value of expected

expected saving reported by respondents).

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maintenance savings variable was set to 15% (i.e., the average value of the

b) Medium policy intervention: the values of all policy variables were set to their

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average values (i.e., 5 points on a 10-point scale), while the value of expected maintenance savings was set to 15% (i.e., the average value of expected savings reported by the survey respondents).

c) Maximum policy intervention: the values of all policy variables were set to their maximum (i.e., 10 points on a 10-point scale), while the value of expected maintenance savings was set to 30% (i.e., the maximum value of

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expected savings reported by respondents). As previously mentioned, F2 (one of two statistically significant explanatory variables in Model 5) is positively associated with several policy instruments (such as

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subsidized loans and grants, building labeling, and tax reductions), and negatively associated with all other incentives (guided tours, courses and workshops, and information about GB benefits). Notably, this factor emerged as a negative predictor

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of GB PP in Model 5 (see Table 5). To account for this association, a fourth intervention package was also designed, considering the observed relationships. In this intervention package, the values of the negatively associated incentives with F2 to minimum (1), and to maximum (10), were set for variables which were found to be positively associated with Factor 2. Concurrently, the value of the expected annual maintenance savings was set to the maximum reported by survey participants (30%). Table 6 reports the values assigned to policy variables, and the estimated size of the GB premium under each scenario. The results show that the predicted size of the acceptable PP ranges between 7.9% in the “no intervention” scenario (which is fairly 17

ACCEPTED MANUSCRIPT close to the average size of the PP indicated by the survey participants) to 15.7% under the fourth, “optimal intervention”, scenario. This indicates that under a careful selection of incentives, the estimated size of the acceptable GB PP can expectedly increase by nearly twofold. Table 6. Scenario assessment of various combinations of GB policy measures Variable value by intervention type

a

Medium intervention

Maximum intervention

Optimal intervention

1 1 1 1 1 1 1 15 7.925

5 5 5 5 5 5 5 15 4.362

10 10 10 10 10 10 10 30 4.374

10 10 10 1 10 1 10 30 15.686

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No intervention

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Improved information sharing Guided tours to GB sites GB courses and workshops Subsidized loans and grants Labeling building according to GB rating Tax reductions More information about GB benefits Maintenance savings (% annual)a Acceptable GB by consumers (%)

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Variable

in the first two scenarios, the value of the maintenance savings variable (% annual) was set to the average value observed in the sample, that is, 15%.

5. Discussion

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As previous studies reveal, homebuyers’ motivational drivers for entering the GB market can be examined from three different perspectives - economic, environmental, and social. Economic motives include reduction of water, energy, and maintenance costs, as well as future changes in property values [77,79,103]. Environmental drivers

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include the reduction of environmental impacts and creation of healthier living conditions. Concurrently, societal considerations may cover, depending on the

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specific interest group, concerns about society and its values, striving for innovation, and improving the societal (or professional) image [75,104]. Some of these factors may be more relevant to specific groups of prospective homebuyers, depending on their personal knowledge and attitude towards GBs, and their willingness to support GBs [26,31].

Overall, survey participants indicated WTP a GB PP in the range of about 7–10%. In comparison to previous studies, this range is higher than the acceptable GB PP of about 5% found among Chinese homebuyers [62]. This difference may be attributed to different values and socio-economic levels, as well as to different familiarity with the GB concept and potential benefits. Indeed, as the survey findings demonstrate, 18

ACCEPTED MANUSCRIPT potential homebuyers who stated lower familiarity with the GB concept, were willing to pay a lower PP then those with higher familiarity. This indicates the importance of providing GB information and education. However, as it turns out, the effect of some GB incentives may be counter-intuitive. Thus, in our study, two variables emerged as significantly associated with the

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acceptable size of GB PP: the expected maintenance savings and provision of economic incentives for GB buyers (P<0.01). Both were anticipated to be positively associated with higher WTP for GB. Nevertheless, while this expectation was realized for the increase in maintenance savings, the delivering of economic incentives

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emerged, quite unexpectedly, with a negative sign, indicating that higher GB incentives to potential homebuyers are likely to lead, all other things held equal, to

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lesser, instead of greater, WTP higher GB PP. Evidently, these results reflect unintended negative consequences of economic interventions for homebuyers’ behavioral change.

Evidence synthesis of the literature did not identify any research investigating unintended consequences of incentive interventions for influencing GB purchasing decisions. However, several explanations can be offered for this unexpected result.

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First and foremost, given high apartment prices, potential homebuyers are naturally eager to reduce expenses. Therefore, they may consider governmental financial incentives as a sign that the state is ready to shoulder additional costs associated with GBs. Another possible explanation is that potential homebuyers may interpret public

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financial incentives as a covert attempt to sell an inferior product, which effectively renders such GB-related economic incentives counter-productive.

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Literature on the mechanisms of action of incentive interventions suggests another explanation, according to which incentives can “crowd out” intrinsic motivation [105106], when financial incentives are viewed as paternalistic or undermining autonomy if recipients think that they are “told what to do” [107]. In other words, at times, financial incentives to homebuyers may be controversial and can generate emotive and opposite responses. Furthermore, according to Gabay et al. [108], from an economic point of view, GBs can justify an additional upfront expense of only 0.12–1.33%. Yet, in our study,

19

ACCEPTED MANUSCRIPT potential homebuyers expressed WTP much higher GB PP, reaching 7–10%. This indicates that considerations for GB-purchasing are not entirely economically rational.

6. Conclusions The study mainly contributes to better-understanding of how potential homebuyers’

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GB choice can be encouraged by applying informed policy tools. In particular, the study extends the state of the art on the subject [77, 82, 87–89] by showing, rather counterintuitively, that once-off, short-term financial incentives are likely to affect negatively the size of the acceptable PP that potential homebuyers are willing to pay

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for GBs (P<0.01). This indicates that apparently, such incentives are not perceived as a long-lasting bond between homebuyers and the government. By contrast, long-term governmental commitments, such as tariff discounts on water and electricity, may be

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perceived by GB prospective buyers as a convincing incentive that may increase their WTP a higher PP. As our analysis indicates, maintenance savings were found to be one of the most significant factors positively associated with the acceptable size of GB premium (b=0.298, t=7.89, P<0.01), indicating that each percent of expected maintenance savings is associated, all other things held equal, with approximately

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0.3% increase in the premium size.

The study suggests that a mix of tangible incentives or rewards - financial and nonfinancial - may be effective in increasing homebuyers’ WTP higher GB PPs. In particular, the message that GBs can effectively reduce future maintenance costs

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should be promoted, considering that homebuyers appear to ascribe high importance to potential maintenance savings, far beyond rational economic considerations. It is

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also recommended, that planning, design, and delivery of future incentives should evaluate unexpected consequences regarding their effectiveness. The study reveals, that there is a clear distinction between self-reported importance of certain GB policy measures, such as short-term financial incentives, and the stated WTP a GB PP. As a result, even if respondents flag financial incentives as highly important (9.04±0.74 points on a 10-point scale assigned to tax reduction and 9.00±0.75 points assigned to subsidized loans and grants), it does not necessarily mean that they are willing to pay a higher GB premium, if such incentives are offered. Hence, the finding regarding the negative effect of direct and immediate financial

20

ACCEPTED MANUSCRIPT support does not, in fact, contradict the stated importance of such incentives by potential homebuyers. The present study was not aimed at developing new GB technical solutions. Yet, shedding a light on the efficacy of GB incentives, may contribute to a wider adoption of GB practices by the construction industry. Furthermore, the methodology applied

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in this study can be used for conducting similar studies elsewhere.

The study has several limitations worth noting. Although survey’s participants were reasonably representative of the general Israeli population, with respect to region of residence, educational level, gender, and age (within the 17–70 age group), its results

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should be considered time and place specific. Therefore, future studies, that can be based on the same methodology, should verify the generality of the detected findings

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and the time consistency thereof. Additionally, the study was based on declared, not revealed, WTP, and as such, it may be subject to estimation biases.

Acknowledgments

The study was supported by the Israel Ministry of Environmental Protection [grant

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[101] Israel Central Bureau of Statistics (ICBS), Statistical Abstract of Israel 2016, Jerusalem, 2017.

[102] S. Vyas, L. Kumaranayake, Constructing socio-economic status indices: How to use principal components analysis, Health Policy Plan 21(6) (2006) 459-68. [103] L. Adityaa, T.M.I. Mahliaa, B. Rismanchic, H.M. Nge, M.H. Hasane, H.S.C.

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Metselaare, M. Oki, H.B. Aditiyab, A review on insulation materials for energy conservation in buildings, Renewable and Sustainable Energy Reviews 73 (2017) 1352–1365.

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[104] J. Grandia, B. Steijn, B. Kuipers, It is not easy being green: Increasing sustainable public procurement behavior Innovation, The European Journal of Social

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Science Research 28(3) (2015) 243–260. [105] B. Frey, Not just for the money: An Economic Theory of Personal Motivation, Brookfield, VT: Edward Elgar Publishing, 1997. [106] E.L. Deci, R. Koestner, R.M. Ryan, A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation, Psychological Bulletin 125 (1999) 627–668. [107] A.N. Fiszbein, N. Schady, N. Schady, F.G. Ferreira, M. Grosh, N. Kelleher, P. Olinto, E. Skoufias, Conditional Cash Transfers: Reducing Present and Future Poverty. A World Bank Policy Research Report, 2009, 47603.

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ACCEPTED MANUSCRIPT [108] H. Gabay, I.A. Meir, M. Schwartz, E. Werzbergera, Cost-benefit analysis of green buildings: An Israeli office buildings case study, Energy and Buildings 76

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ACCEPTED MANUSCRIPT Appendix A: Survey questionnaire Part I 1. Do you intend to buy an apartment in the next five years? Yes

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I am familiar with the concept of "green building" Green building may lead to savings in maintenance expenses, as a result of reduced energy demand for lighting, heating and air conditioning Green building may lead to water savings through grey water recycling and use for irrigation, toilet flushing etc. Green building encourages reduction of water use during construction Green building encourages reduction of energy consumption during construction Green building may reduce the amount of construction waste Green building offers a healthier indoor environment Green building uses land more efficiently Green building uses healthy and environmentally friendly construction materials

Disagree 1 2

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2. On a scale of 1 (disagree) to 10 (strongly agree), please rate the extent to which you agree or disagree with the following statements:

Part II

3. On a scale of 1 (will not affect at all) to 10 (will strongly affect), please rate the extent to which the following measures may affect your willingness to purchase a house or an apartment rated as a green building, instead of purchasing a similar conventional house or apartment: 1

1

2

Providing the public with more and better information about green building benefits Organizing guided tours to green building sites and houses

2

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Will not affect at all 1 2 3 4 5

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ACCEPTED MANUSCRIPT Providing courses and workshops on green building Subsidizing loans and grants for purchasing certified green building house/apartment Labeling new and existing buildings according to their compliance with green building standard Granting tax reducing to green building homebuyers Advertising and educating about green building advantages

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4. In the table below, please choose your estimation of the cost premium (to the developer) for constructing a new house or apartment rated as a green building, in comparison with constructing a similar conventional house or apartment: 9%

10%

20%

21%

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5. In the table below, please choose the maximum price premium that you will be willing to pay for purchasing a new house or apartment rated as a green building, in comparison with purchasing a similar conventional house or apartment: 1%

2%

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5%

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8%

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6. In the table below, please choose your estimation of the maximum percentage of expected saving in annual maintenance expenses in a new house or apartment rated as a green building, in comparison with a similar conventional house or apartment: 0%

1%

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5%

6%

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ACCEPTED MANUSCRIPT 7. In the table below, please choose your estimation of the payback period required for recovering the cost premium of a green building house or apartment through maintenance savings: 1 year

2 years

3 years

4 years

7 years 13 years 19 years

8 years 14 years 20 years and more

9 years 10 years 11 years 15 years 16 years 17 years Will never return the cost premium

Part IV 8. Please answer the following demographic questions:

What is your gender?

Male

What is your age range?

20-29

What is your education level? How many years of schooling do you have? What is your marital status?

South

What is your ethnic identity?

30-39

40-49

50-59

High School

Professional, Practical engineer

Single

Married

Divorcee

Yes, no. of Children: ____________ Jewish Arab

Widower

Other ___________ Religious Very tradition religious

Not religious observes

When do you intend to buy a house or an apartment?

I do not intend to buy

During the coming year

Will you have to take a mortgage for purchasing a house or an apartment?

Yes

Do you currently own a house or an apartment?

Yes, only one

Yes, more than one

What is your net monthly income (in NIS)?

Up to 6,000

12,00018,000

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70 and above Academi c degree

No

Not religious secular

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Elementary

What is your degree of religiosity?

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Do you have children?

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What is your region of residence?

5 years

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months to year 6 years 12 years 18 years

Within two to five years

In more than five years

No

6,00012,000

No

18,00024,000

Over 24,000

ACCEPTED MANUSCRIPT Appendix B: Bivariate Pearson correlation coefficients between public policy variables I2

I3

I4

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I6

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I1 I2 I3 I4 I5 I6

.718**

.533** .661**

.496** .455** .322**

.616** .649** .592** .446**

.435** .380** .295** .742** .432**

.635** .638** .633** .427** .667** .428**

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Notes: **Indicates a 0.01 significance level; see Table 3 for variables codding.

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Highlights • The study examines the acceptable size of green buildings (GB) price premium.

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The price premium size was co-analyzed with the main GB incentives to homebuyers. Direct financial incentives were associated with lower acceptable price premium. Maintenance assistance was found to increase the acceptable size of price premium. A mix of financial and non-financial incentives is suggested to stimulate GB choice.

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•