Housing Wealth Distribution, Inequality and Residential Satisfaction, 1997-2008

Bao, Helen (2024). Housing Wealth Distribution, Inequality and Residential Satisfaction, 1997-2008. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-856273

Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides.

The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom.

Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature.

The overarching research question of this project is whether and how behavioural insights can be used to help rural residents in China make sound financial decisions, which will ultimately contribute to the sustainable economic development in China. The research will be conducted through field experiments in rural China. By relying on field evidences, the project team will develop policy tools and checklists for policy makers to help rural households make sound financial decisions. Two types of tools will be developed for policy makers, namely, "push" tools that aim to achieve short-term policy compliance among rural households so that they can break out of the persistent poverty cycle and "pull" tools that can reduce fraud, error, and debt among rural households to prevent them from falling back into poverty. Finally, the project team will also use the research activities and findings as vehicles to engage and educate rural residents, local governments, regulators, and financial institutions. Standard and good practice will be proposed to interested parties for the designs of good behavioural interventions; ethical guidelines will be provided to encourage good practice. This important step ensures that the findings of this project will benefit academia and practice, with long-lasting, positive impacts.

The findings will benefit researchers in behavioural finance and economics, rural economics, development economics, political sciences, and psychology. The findings of and the engagement in this project will help policy makers to develop cost-effective behavioural change policies. Rural households will benefit by being nudged into sound financial decisions and healthy financial habits. The project will provide insights on how to leverage behavioural insights to overcome persistent poverty in the developing world. Therefore, the research will be of interest to communities in China and internationally.

Data description (abstract)

This dataset encompasses the foundations and findings of a study titled "Housing Wealth Distribution, Inequality, and Residential Satisfaction," highlighting the evolution of residential properties from mere consumption goods to significant assets for wealth accumulation. Since the 1980s, with financial market deregulation in the UK, there has been a noticeable shift in homeownership patterns and housing wealth's role. The liberalisation of the banking sector, particularly mortgage lending, facilitated a significant rise in homeownership rates from around 50% in the 1970s to over 70% in the early 2000s, stabilizing at 65% in recent years. Concurrently, housing wealth relative to household annual gross disposable income has seen a considerable increase, underscoring the growing importance of residential properties as investment goods.

The study explores the multifaceted impact of housing wealth on various aspects of life, including retirement financing, intergenerational wealth transfer, health, consumption, energy conservation, and education. Residential satisfaction, defined as the overall experience and contentment with housing, emerges as a critical factor influencing subjective well-being and labor mobility. Despite the evident influence of housing characteristics, social environment, and demographic factors on residential satisfaction, the relationship between housing wealth and satisfaction remains underexplored.

To bridge this gap, the research meticulously assembles data from different surveys across the UK and the USA spanning 1970 to 2019, despite challenges such as data compatibility and measurement errors. Initial findings reveal no straightforward correlation between rising house prices and residential satisfaction, mirroring the Easterlin Paradox, which suggests that happiness levels do not necessarily increase with income growth. This paradox is dissected through the lenses of social comparison and adaptation, theorizing that relative income and the human tendency to adapt to changes might explain the stagnant satisfaction levels despite increased housing wealth.

Further analysis within the UK context supports the social comparison hypothesis, suggesting that disparities in housing wealth distribution can lead to varied satisfaction levels, potentially exacerbating societal inequality. This phenomenon is not isolated to developed nations but is also pertinent to developing countries experiencing rapid economic growth alongside widening income and wealth gaps. The study concludes by emphasizing the significance of considering housing wealth inequality in policy-making, aiming to mitigate its far-reaching implications on societal well-being.

Data creators:

Creator Name	Affiliation	ORCID (as URL)
Bao Helen	University of Cambridge	https://orcid.org/0000-0003-3966-3867

Sponsors:

Economic and Social Research Council

Grant reference:

ES/P004296/1

Topic classification:

Social welfare policy and systems
Housing and land use
Social stratification and groupings

Keywords:

BEHAVIOURAL SCIENCES

Project title:

Nudging towards a better financial future: applying behavioural insights in the development of financial systems in rural China

Grant holders:

Helen Xiaohui Bao, Colin Lizieri

Project dates:

From	To
0001	0031

Date published:

02 May 2024 16:48

Last modified:

02 May 2024 16:48

Coverage and Methodology

Access and Administration

Data sourcing, processing and preparation:

We test the hypotheses by using data from the British Household Panel Survey (BHPS) between 1997 and 2008, when both residential satisfaction scores and home valuations are available. BHPS has been used extensively in environment and urban studies (see, for example, BAO and LI, 2020; CORRADO et al., 2013; HAND, 2020). In 2009, BHPS is merged into a larger longitudinal survey project, Understanding Society. We use the data before the transition to ensure the consistency of the data. We include homeowners (about 73% of all respondents during our sampling period) because the home valuation question was only asked to this group of respondents. Our dataset contains 99,701 observations from 18,359 individuals across the UK. Variable definitions and descriptive statistics can be found in Table 1. The Dependent Variable We use the answers to the question “How dissatisfied or satisfied you are with your house/flat?” as the measurement of residential satisfaction. The answers are coded from 1 to 7, with 1 being “not satisfied at all’ and 7 being ‘completely satisfied’. The average value of residential satisfaction (HOUSAT) is 5.59 during the sampling period. Although both household income and home value increased steadily over the 12-year period, satisfaction scores remained remarkably stable over the time. Control Variables We include three groups of control variables in order to reliably isolate the net effects of social comparison and adaptation. The first group of control variables are demographic and socioeconomic indicators such as annual household income (INCOME) and household type (e.g., COUPLE and SINGLE). We also include the total number of children (NUMKIDS) and whether the household has a new baby (NEWBORN) in the survey year, because the literature suggests that these are important factors that affects residential satisfaction. The second group of control variables includes objective measurements of the residential environment. These variables are derived from questions to which the respondents can provide relatively objective answers, such as whether the respondents are still paying a mortgage on their homes (i.e., MORTGAGE = 1) and whether the accommodation has central heating (CENTRAL). We also considered the effect of recent moving on residential satisfaction, as suggested by the literature. Specifically, variable MOVER equals one for individuals who changed home address during the survey year. The last group of controls consists of subjective measurements of the residential environment. We use FINNOW to capture the current financial situation of a household. It is based on the question “"How well would you say you yourself are managing financially these days?” We also define FINFUTURE based on the question “Looking ahead, how do you think you will be financially a year from now?”. This variable reflects the expectation an individual has about her financial situation in the coming years. There are three variables to gauge the level of noise from neighbours or street and the shortage of space. Note that these three variables are constructed based on the respondents’ perception instead of objective measurement of noise and space. For example, variable NEIGHNOI measures the level of noise from neighbours. It is based on the question “Does your accommodation have any of the following problems: Noise from neighbours?”; someone who plays rock music occasionally may be classified as a noisy neighbour by a mother of a young baby, but not by a college student who parties hard. Hence, the answers to this question are subjective measurements of neighbourhood noise level. The inclusion of a comprehensive set of controls over housing characteristics is critical to isolate the net effect of housing wealth on housing satisfaction. During our sampling period, the UK housing market experienced significant growth while the real income level did not. Coupled with the inelastic housing supply, this caused some households to struggle to climb the property ladder. The mismatch between the characteristics of houses available and within reach and the demand from these households may confound the estimation of housing wealth effects. We control for this factor by including the relative measurement of financial situation and housing quality. Although not an exhaustive list of housing attributes, the included variables cover the most important aspects of housing needs. Therefore, the identified relationship between housing wealth and housing attributes is unlikely to be significantly affected by omitted variable bias. This issue is further addressed in the robustness checks section. Housing Wealth We use the answers to the question, “About how much would you expect to get for your home if you sold it today?” as the measurement of housing wealth in this study. This subjective assessment of home value has two advantages. Firstly, professional house valuation is not included in the BHPS dataset, and is challenging to derive from other data source. Using perceived house values from the same dataset ensure the consistency and reliability. Secondly, most homeowners won’t sell their houses; they are not experienced enough to have a fair valuation of their home either. Their perceived value and the market value of their home do not necessarily agree. Residential satisfaction is more responsive to perceived home value than market valuation, because the former is more salient and available for homeowners. The estimated home value (VALUE) averages £145,589 between 1997 and 2008. This slightly higher than the national statistics, which is about £128,000 according to the Office of National Statistics (ONS). We compare the trend of average home values from our sample and ONS house price index. The BHPS figures are generally higher than those from the ONS. The discrepancy could come from multiple sources, such as homeowner’s tendency to overestimate their home value, a higher turnover ratio of cheaper (perhaps smaller) houses, and hence an overrepresentation of such properties in the ONS statistics. However, the long-term trend is consistent between the two series. Because we are investigating the long-run relationship between housing wealth and residential satisfaction, this consistent over-estimation of home value will not affect our conclusions. Self-comparison Measurements Individuals make comparison not only to others but also to their own past or status quo. We introduce two self-comparison measurements based on INCOME and VALUE in order to understand how much the relative loss aversion effect comes from own income and house value. To separate out individuals whose income or home values fell from one year to the next, we created two self-comparison dummy variables, i.e., INCOMEloss = 1 if INCOMEt < INCOMEt-1, and 0 otherwise. VALUEloss = 1 if VALUEt < VALUEt-1, and 0 otherwise. About 27% of the respondents experienced losses of income from previous survey year, and about 15% of the respondents estimated that their houses depreciated from previous survey year. Social Comparison Measurements The current literature does not provide guidelines regarding how social comparison groups are determined. We assume that people make reference to other individuals within the same age group, with similar education background, living in the same region, or working in the same type of jobs. Housing consumption is measured relative to the average level of consumption in one’s reference groups. Those who consume significantly less/more than their peers will be classified as worse-off/better-off group. Individuals cannot accurately estimate what their reference group believes their houses are worth. They can only estimate what their housing wealth position roughly is within their reference groups. To take into account the ambiguity and uncertainty in the estimation of one’s relative social position in a reference group, we assume that the reference point in social comparison should be a value range instead of a specific value. We define the worse-off groups to include those individuals whose housing wealth is below the 25th percentile within their reference groups and better-off groups to include those with housing wealth above the 75th percentile. The reference point in this definition is the 50% of individuals whose housing wealth level is considered to be average or typical. For age, as an example, we allocate individuals in the six age groups as defined in Table 1. Within each group, if an individual’s house value is below the 25th percentile of the house values in her age group (i.e., her house value is lower than 75% of the people in her age group), she will be identified as worse-off, and 〖LOW〗_age= 1. If on the other hand, an individual’s house value is above the 75th percentile of the house values in her age group (i.e., her house value is greater than 75% of the people in her age group), she will be identified as better-off, and 〖HIGH〗_age = 1. Using the same method, we define three more sets of social comparison indicators based on education (three groups), socioeconomic status (four groups), and region (19 groups), respectively. This gives six more social comparison variables: 〖LOW〗_edu, 〖HIGH〗_edu, 〖LOW〗_se, 〖HIGH〗_se , 〖LOW〗_reg, and 〖HIGH〗_reg. This multi-dimensional approach of social comparison measurement has two advantages. First, it helps us to identify where and how social comparisons are made. The determination of 〖SC〗_(i,t) in Equation (4) is not a black box. Secondly, it also helps to establish the robustness of the relative residential satisfaction theory, if we can find that the effect is present in most or even all of the social comparison groups considered.

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