Immigration and house prices under various labour market structures in England and Wales

Abstract

Chinese

This article estimates the impact of immigration on local house prices under various local labour market structures in England and Wales. Typically, for the nation as a whole, newly arriving immigrants add to the overall housing demand; this would in general push up house prices when facing an upward-sloping supply curve. However, sorting and native-outmigration response to immigration may change the dynamics and impact at the ‘local’ level, depressing house prices through income change. We use data on England and Wales to investigate the local house price effect of immigration when taking into account the local labour market structure of the areas, particularly with respect to employment density and average socioeconomic profile (skill) of workers. We found that in high density of employment areas but with a majority of the occupations in low skill sets, there is a negative house price effect led by immigrant inflows, and this might be due to a type of tenure ‘downgrade’ in the area as immigrants increase the rate of free renting. Free renters are less likely to participate in the housing market themselves and an increase in the rate of this form of tenure could also reduce their mobility further, hence lead to lower levels of housing stock turnover and transaction-related renovation; as a result, both housing quality and house prices fall. The evidence is in addition to the native flight argument typically found in the literature to explain house price depreciation led by immigration.

Keywords

agglomeration/urbanisation demographics economic processes house prices housing immigration migration

Introduction

Immigrants affect the lives of locals through a variety of ways such as house prices, crime, competition for jobs, and educational and welfare resources; therefore, concerns about their potential negative impacts have become particularly acute during periods of increased migration flows. The article puts its focus on how immigrants impact on local housing markets under various regional labour market structures, and several channels are empirically tested.

So, how would immigration impact the housing market? Ostensibly, one might expect immigration to increase demand for housing, leading to price rises particularly in an economy with inelastic housing supply. While a positive house price effect might be expected at the macro level (Sá, 2014; Saiz, 2007), there are factors at work at the local level which might mitigate these impacts. The extensive literature on ‘white flight’ (Braakmann, 2016; Sá, 2014; Saiz and Wachter, 2011), for example, suggests that the influx of an outside group into a neighbourhood can cause outflows of indigenous residents. In this case, the value of properties could be reduced by immigrants through a reduction in the area-level aggregate income and/or neighbourhood stability (coherence).

However, would the effect be prevalent throughout different types of local labour market structures? In other words, would native people always respond to immigrant inflows by out-migration? The article considers the element of local labour market structure from the perspective of employment density and the average socioeconomic profile (skill) of workers in that place. Using these two characteristics, four subregion types are defined: the high density and high skill, the high density and low skill, the low density and high skill and the low density and low skill. Distinguishing areas along these lines may help us observe native and immigrant workers with distinct sets of preferences and behaviours which could potentially lead to alternative interaction – which does not have to be residential sorting or native flight. For areas with high employment density but jobs mainly in low skillsets, we provide some evidence such that immigrants may downgrade the tenure on average by increasing the rate of free-renting; the phenomenon may depress the general desire of a neighbourhood to demand more housing, and contribute to the house price reduction. The existing literature has often focused on the residential sorting made by natives because of a homophily process – a general preference towards residing close to those who share similar ethnic backgrounds (Saiz, 2007; Saiz and Wachter, 2011), alternative processes may also happen such as accommodation sharing among immigrants and natives; in areas with high job density and low skills, mobility could be restricted to the nature of the employment type. House prices could be reduced as the area tends to hoard workers who lack channels to move elsewhere and the overall level of property transactions within the area could be reduced. Therefore, the current paper seeks to address the gap in the potential economic process behind the immigration–house price link.

In the following section we review the current literature, establishing the motivation for the actual analysis and for the method employed in the paper. The next section describes the set of mechanisms which help shape our hypotheses. Then we set out the specific modelling strategy. Data description and summary statistics are provided in the following section and findings are presented in the penultimate section. Finally, we conclude with a brief summary of our findings and their implications.

Literature review

While the majority of the literature on immigration impact has focused on labour market outcomes (Card, 2001; Dustmann et al., 2013), robust empirical estimation on the housing market effect of immigration has been on the rise in recent years (Akbari and Aydede, 2012; Gonzalez and Ortega, 2013; Saiz, 2007).

Given that housing is an important sector in its own right, generating significant employment and trade through construction (Akbari and Aydede, 2012) and transactions-related industries (estate agency, surveying, conveyancing, and mortgage finance), the housing impact of immigration is potentially a significant component of the overall economic impact of immigration. Additionally, immigrants tend to cluster spatially because of their shared language, culture and lifestyles (Meen et al., 2016; Munshi, 2003; Saiz, 2007); therefore, they may collectively exert a considerably large influence in certain housing submarkets and regions (Saiz, 2007: 5).

In the UK, the majority of immigrants concentrate in London boroughs: for example, in 2015, Harrow, Brent, Newham and Westminster all have a share of foreign-borns of over 50%; outside London, local authorities such as Leicester, Luton and Slough also have a share of immigrants of over 30% (source: UK Quarterly Labour Force Survey). Overall, immigration in the UK as a fraction of the working population rose from 8% in the mid-1990s to 16% in 2015. Meanwhile, seasonally adjusted average house prices have increased from £60,000 in 1995 to £215,000 in 2015 (source: Land Registry data), raising concerns about the impact of immigration on housing affordability, such as the claim by the former Home Secretary that ‘without the demand caused by mass immigration, house prices could be ten per cent lower over a twenty year period’ (May, 2012). However, even if this claim could be substantiated at the macro level, it would belie a complex geography of varying house price effects at the local level potentially, because of the different dynamics of local economies.

Why is the local labour market structure of areas important in examining the immigration impact on local house prices? Traditional argument has often indicated a native out-migration phenomenon which is responsible for a local house price reduction (Sá, 2014; Saiz and Wachter, 2011). Both theories and empirical studies show that house price depreciation is considered a premium that natives are willing to pay for segregation. However, the kind of native–immigrant interaction through the residential housing market might not be saliently observed when areas are divided into different local labour market types. The article attempts to explore how the effect would vary over these different types of structures. Specifically, the level of employment density and average socioeconomic profile of the working age population are chosen to characterise the areas into different types of local labour markets. We consider these structures to be relatively fixed when compared with population movements. Therefore, native and immigrant interaction through both the labour markets and the housing markets can be analysed jointly under the different types of local economies. We assume that individuals would self-select into the areas according to their own distinct set of preferences, motives and behaviours, which would eventually lead to differing patterns of interaction; in turn, the influence on local house prices would be different. We are interested in testing these empirical relationships.

Closely related work includes Braakmann (2016), who examined the house price impact of immigration at different price quantiles. It was found that house price is depressed only at the lower end of the distribution up to the median, but there is no impact on property prices above the median. His explanation is not limited to native out-mobility, but also to the changing usage of housing space. In particular, existing landlords turn owner-occupied properties into flats to accommodate more immigrants. While our paper also used tenure of immigrants to explain housing value depreciation, it is a completely different channel; since we follow Sá to control for quality of housing in the model and to use quality adjusted house price index (corrected for housing space, sizes and number of rooms, etc.) instead of price paid data from the Land Registry, the possibility that housing values depreciate through a shrinkage of the average size of the properties in a neighbourhood is eliminated in our set of explanations.

Mechanisms

The mechanism behind reduction in house prices often involves a native–immigrant interaction through the residential housing market. It was found that natives move out of a neighbourhood in response to an inflow of migrants, that is, native flight (Borjas, 2003; Borjas et al., 1997; Card, 2001; Saiz, 2007). This is usually attributed to homophily – preference of residing close to those in the same ethnic group and/or socioeconomic group. Crucially, native outflow may alter the total income distribution of a neighbourhood, thereby affecting house prices. For example, if the number of natives who leave the area is greater than the number of migrants moving in, total income in an area would fall, leading to a reduction in housing demand and, given that the housing market faces an upward-sloping supply curve (Ball et al., 2010; Barker, 2004; Bramley et al., 2008; Pryce, 1999), house prices are reduced through an income effect. Even in the situations where the number of natives moving out is exactly displaced by the number of incoming immigrants, or the number of natives moving out is smaller than the number of immigrants moving in, if the new households tend to have lower incomes (relative to the more affluent native households), there could still be a reduction in the overall income in the area, leading to a fall in both housing demand and house prices.

Beyond the mainstream channel through which house prices are affected by immigration, further research is needed regarding the roles that area-level job density and socioeconomic profile of the workers play in the immigration–house price link. To examine house price effect under various labour market structures, the areas are classified using the skill distribution (socioeconomic profiles) of workers and the employment density of the areas. In this case, we would have four subgroups, that is, areas with high employment density and high skill professions, areas with high employment density but low skill professions, areas with low employment density but high skill professions, and lastly areas with low employment density and low skill professions. We investigate the house price impact of immigration in each of these subgroups and analyse why the effects would be different from each other.

Three channels are sought to explain the differentials in the house price effect of immigration. The first one is on native out-migration response, the second one is about pressure on native wage and the last is on tenure and property type ‘downgrade’.

Pressure on native wage has been outlined in studies including Sá (2014) and Dustmann et al. (2013) in detail. Specifically, if immigrants reduce the income of native people, the overall purchasing power of housing would be reduced therefore leading to a fall in demand for housing. House prices therefore drop.

Perhaps, what is more interesting is the area-level change in the form of tenure, which could potentially explain housing value depreciation. Areas with high job density and less skilled workers tend to observe a higher rate of free renting, but a lower rate of owner occupation as the purpose of staying is more likely to be employment-focused rather than residence-focused. Immigrants in this type of area are likely to cause a shift towards this form of tenure: on one hand, to stick close to their co-ethnic ties through accommodation sharing; on the other hand, to save costs on housing consumption as a result of lower income. When compared with their native counterpart (i.e. less skilled native workers) in high job density areas, their relatively fewer ethnic/social ties elsewhere in the UK would limit and discourage mobility to search for alternative opportunities; when compared with high skilled migrant workers in high job density areas, whose internal mobility is usually facilitated by employer organisations and educational institutions, their mobility and ability to gain information are also lower. Therefore, the group is unlikely to realise step changes in financial gains in the long run, which would potentially result in a persistently weaker housing demand, which is perhaps one of many subsistence needs that has been compromised.

Overall, if immigrants lead to an increase in the rate of inflows of free-renters and social housing dwellers, they are likely to reduce house prices through:

overall reduced level of knowledge to participate in the housing market activities, therefore reducing housing demand;

overall reduced level of mobility, which in turn decreases the rate of housing stock turnover; therefore, further decreasing transaction-related housing renovations or upgrade, causing a subsequent drop in housing quality and house prices.

Overall, we use data to provide some empirical evidence on residential sorting, wage competition and tenure ‘downgrade’, in order to explain the differing impact of immigration on local house prices in different types of local labour market types.

Methodology

To examine the role played by area-level employment density and skill characteristics in how immigrants affect local house prices and other housing market variables, the local authorities in England and Wales are divided into categories according to these characteristics. Specifically, the level of job density is used to capture employment density and also an index is derived to characterise the average socioeconomic ranking for each area. A base year in 2001 is chosen to mitigate the endogeneity problem as it is outside the study period between year 2003 and year 2010; therefore, these variables are treated as relatively fixed over time compared with other factors inside the model (Appendix P1). The two measures in algebraic form are:

A : = {(\frac{J}{Pop})}_{i, 2001}

(1)

The base year job density A is calculated using the number of jobs (J) divided by the local authority population (Pop) in 2001.

B : = \sum_{i = 1}^{8} \frac{S_{i}}{i}

(2)

The skill index B is derived from the socioeconomic variable from NOMIS – Official Labour Market Statistics in 2001. The variable has eight categories and it includes: higher managerial and professional, lower managerial and professional, intermediate occupations, small employers and own account workers, lower supervisory and technical, semi-routine occupations, routine occupations, and never worked and unemployed. The variable is ordinal with the higher managerial and professional being the highest level of socioeconomic status and the never worked and unemployed category being the lowest level of socioeconomic status. So the skill index is a weighted average of the percentage population at each skill level; each level is weighted by its rank, such that the highest socioeconomic status is given the most weight while the lowest socioeconomic status is given the least weight. The index represents the average skill level for the area. In fact, it is also an indication of the average size of the businesses since not all companies would cover the full socioeconomic rank, that is, many local trades would not have managerial positions.

Overall, we first divide the local authorities into low and high employment density areas using the job density variable and then further incorporate the skill index to classify the areas into high job density and high skill, high job density and low skill, low job density and high skill and lastly low job density and low skill areas. In each of these subgroups, we examine the immigration impact on local house prices and other housing market variables.

To model house price impact of immigration, a spatial panel approach is used (Dustmann et al., 2008) and it involves a first differenced two-stage least squares (2SLS) specification with a settlement pattern instrumental variable to exogenously predict the immigration variable.

Δ \ln (P_{iqt}) = β \frac{Δ Im m_{iqt}}{Po p_{iqt - 1}} + γ X_{iqt - 1} + ϕ_{t} + (ρ_{i}) + Δ ε_{iqt}

(3)

The model follows closely with that in Sá (2014). The regression shows that the change in the log of the house price index is a function of the change in the stocks of immigrants as a percentage of local initial population represented by $\frac{Δ Im m_{iqt}}{Po p_{iqt - 1}}$ and various other controls represented by X_iqt−1. These include: the local unemployment rate, to control for local macroeconomic conditions; the share of the local population claiming state benefits; the crime rate, which may affect housing demand; the number of dwellings divided by local population, to capture the level of local housing supply; and an index capturing the average quality of housing in each local authority. The controls are lagged by one year to reduce the potential endogeneity between the controls and the immigration.

The subscript q represents the subgroup in which the model analyses the effect: the set of results examines the effect in four subgroups using both the employment density and the socioeconomic characteristic. Potential explanations of why there are differing results in subgroups are searched. The model also incorporates time effects $(ϕ_{t})$ to capture the national trends in inflation and other economic variables.

The instrument (IV) is formulated based on the past settlement pattern of different immigrant groups in each local authority. It is the dominant methodology in the economic literature on immigration impacts; notable works include Card (2001) and Saiz (2007). Bartel (1989) has argued that immigrants in the USA tend to settle in areas where immigrant settlement is already strong. Immigrant networks are an important determinant of locational choices of new immigrants as they facilitate the job search process and assimilation into a new culture (Munshi, 2003).

In particular, the IV is defined as:

\frac{\sum_{c} λ_{ci t_{0}} Δ Im m_{it}}{Po p_{it - 1}}

(4)

where $λ_{ci t_{0}}$ is the share of individuals born in foreign region c who settle in local authority i in base year t₀; this provides a relative size of the network in each local authority from each ethnic group c with respect to the country as a whole. $Δ F B_{it}$ is the change in the stock of immigrants from ethnic group c in the UK as a whole in year t. Therefore, $λ_{ci t_{0}} Δ Im m_{it}$ is the predicted change in the stock of immigrants from ethnic group c in year t that choose to live in local authority i. Summing across all ethnic groups would yield a measure of the expected change in the total stock of immigrants in local authority i in year t. Specifically, nine foreign regions are considered: Americas and Caribbean, Africa, India, Republic of Ireland, Other Middle East and Indian Sub-Continent, Asia, Antarctica and Oceania, Europe, and other countries.

Data and descriptive statistics

We draw on a variety of data sources to estimate our model. The immigrant and native population information are obtained using a special licence version of the UK Quarterly Labour Force Survey (QLFS).

House price data at local authority level were based on Land Registry house price indices, which are seasonally adjusted and also correct for changes in the quality of housing. Socioeconomic controls such as unemployment rate, crime rate, and housing quality are collected from various sources including the QLFS, NOMIS, and Home Office, Department for Communities and Local Government and BHPS. This yielded a combined dataset of 170 local authorities spanning 8 years between 2003 and 2010.

Summary statistics are presented in Table 1, and more details on variable sources and construction are available in the Appendix.

Table 1.

Descriptive statistics.

Variable	Observations	Mean	SD	Min	Max
$Δ Im m_{it} / Po p_{it - 1}$	1190	0.002	0.043	−0.260	0.283
$Δ N_{it} / Po p_{it - 1}$	1190	−0.023	0.081	−0.274	0.536
Unemployment rate	1190	0.059	0.025	0	0.176
Δlog house price index	1190	0.035	0.089	−0.176	0.338
$Δ Dwellingstoc k_{it} / onspo p_{it - 1}$	1190	0.004	0.002	−0.008	0.015
Benefits rate	1360	0.149	0.048	0.049	0.309
Crime rate	1360	102.591	39.546	40.755	408.281
Dwelling stock/population	1190	0.435	0.024	0.353	0.530
Index of housing quality	1336	8.106	0.543	4	9

Results

Main results: The role of employment density and socioeconomic status

When taking both employment density and socioeconomic characteristics into account, areas are divided into four categories: low job density but high skill, high job density and high skill, low job density and low skill and high job density and high skill. The house price effects of immigration are tabulated in Table 2. Specifically, house price reduction only took place in high job density but low skill areas, not in the rest of the types. In particular, an increase in the stocks of immigrants had led to a 0.8% decrease in local house prices. There could be several potential explanations behind the house price decrease in this type of area. We offer three and provide empirical evidence on them.

Table 2.

Local house price effects stratified by employment density and socioeconomic status.

	Low job density, high skill		High job density, high skill
	OLS	IV	OLS	IV
Change in share of immigration	−0.054	−0.215	−0.041	−2.276
	(−0.065)	(−0.507)	(−0.044)	(−1.940)
Observations	301	301	292	292
R_squared	0.870	0.864	0.873	–
	Low job density, low skill		High job density, low skill
	OLS	IV	OLS	IV
Change in share of immigration	0.002	−1.350	−0.132 ^**	−0.797 ^***
	(−0.070)	(−1.364)	(−0.065)	(−0.290)
Observations	290	290	283	283
R_squared	0.923	0.665	0.835	0.742

Notes: IV is the instrumental variable based on the historical settlement pattern of different country of origin groups. Both models cluster standard errors at local authority level and they are included in the parentheses. The local authorities are grouped into four categories using two characteristics – employment density captured by the number of jobs over population and a skill index derived from the socioeconomic characteristic of the area. Statistical significance is represented by star symbols where ^* indicates p < 0.1, ^** indicates p < 0.05, ^*** indicates p < 0.01. The first stage coefficients for the immigration variable from top left to bottom right are 0.900^*(4.54), 0.593 (44.33), 1.442 (10.84), 1.817^*** (10.45) – First stage F-statistics are displayed in parentheses. The models are run without the set of socioeconomic controls.

Bold type show the statistically significant results.

(1) Native out-migration response to immigration

When stratifying the areas by employment density and socioeconomic status, there is evidence of native displacement of immigration in all areas apart from the high-density-high-skill areas according to the IV results in Table 3. However, the effects are not significant, therefore cannot be used to explain the housing value depreciation found for high-density-low-skill areas. We conclude that there is limited native mobility in response to newly arriving immigrants in this type of area.

Table 3.

Subgroup immigration impact on native out-mobility 2003–2010.

Dependent variable: Change in share of native population
	Low job density, high skill		High job density, high skill
	OLS	IV	OLS	IV
Change in immigration share	−0.038	−0.586	0.152	0.063
	(0.103)	(0.603)	(0.180)	(0.317)
Observations	301	301	294	294
R_squared	(0.125)	(0.010)	0.126	0.123
	Low job density, low skill		High job density, low skill
	OLS	IV	OLS	IV
Change in immigration share	−0.310^***	−1.913	−0.149	−0.683
	(0.102)	(1.740)	(0.137)	(0.457)
Observations	299	299	294	294
R_squared	0.081	–	0.124	0.066

Notes: IV is the instrumental variable based on the historical settlement pattern of different country of origin groups. Both models cluster standard errors at local authority level and they are included in the parentheses. The local authorities are grouped into four categories using two characteristics – employment density captured by the number of jobs over population and a skill index derived from the socioeconomic characteristic of the area. Statistical significance is represented by star symbols where ^* indicates p < 0.1, ^** indicates p < 0.05, ^*** indicates p < 0.01. The first stage coefficients from top left to bottom right are: 0.472^* (1.64), 0.808^* (5.05), 0.784 (1.32) and 1.550^** (4.54). The models are run without the set of socioeconomic controls.

(2) Pressure on native wage

Robust evidence on the native wage argument in the context of the UK could be found in Dustmann et al. (2013). It is shown that natives at the lower end of the wage distribution often face competition against incoming migrant workers and these workers depress the wages of local people by providing an extra supply to the existing labour force. Lower wage means lower overall income in an area, therefore leading to lower demand for housing. House prices reduce through an income effect.

When searching for potential pressure on native wage in any of the density–skill subgroups, no robust evidence is found, as shown by the results in Table 4, and it does not seem to help explain the house price reduction in high job density but low skill areas either.

Table 4.

Subgroup immigration impact on local native wage, 2003–2010.

Dependent variable: change in log median native wage
	Low job density, high skill		High job density, high skill
	OLS	IV	OLS	IV
Change in share of immigration	0.045	−0.067	−0.216	0.222
	(0.123)	(0.557)	(0.272)	(0.760)
Observations	301	301	294	294
R_squared	0.044	0.041	0.110	0.058
	Low job density, low skill		High job density, low skill
	OLS	IV	OLS	IV
Change in share of immigration	0.073	2.306	0.191	−0.368
	(0.129)	(2.103)	(0.121)	(0.605)
Observations	299	299	294	294
R_squared	0.094	–	0.076	–

Notes: IV is the instrumental variable based on the historical settlement pattern of different country of origin groups. Both models cluster standard errors at local authority level and they are included in the parentheses. The local authorities are grouped into four categories using two characteristics – employment density captured by the number of jobs over population and a skill index derived from the socioeconomic characteristic of the area. Statistical significance is represented by star symbols where ^* indicates p < 0.1, ^** indicates p < 0.05, ^*** indicates p < 0.01. The first stage coefficients from top left to bottom right are: 0.472^* (1.64), 0.808^* (5.05), 0.784 (1.32) and 1.550^** (4.54). The models are run without the set of socioeconomic controls.

However, one may not rule out completely a negative impact on total income explanation. It is possible that the wages of existing immigrants are lowered by new immigrants. Since those who are in charge of relatively abundant capital would divide up the existing job units and let the immigrants share the job but receiving a lower wage, and the low-skilled new migrant workers become a strain upon joining the local businesses because those at the managerial level need to accommodate them but could not do so efficiently, the overall income could still decrease owing to lower immigrant income, generating weaker demand for housing. The small counts of wage data on immigrants from the Labour Force Survey, however, make it directly untestable: either on the impact of existing migrants' income or on the ‘downgrade’ of jobs.

(3) Tenure ‘downgrade’

From Table 5, a more robust piece of evidence comes from the tenure ‘downgrade’. When examining the impact of immigration on the percentage change in different forms of tenure, the results are tabulated as follows.

Table 5.

Impact of immigration on local growth of different forms of tenure, 2003–2010.

	Low job density, high skill		High job density, high skill
	OLS	IV	OLS	IV
Owner-occupied tenure	0.814^***	−0.490	0.465^***	0.466
	(0.125)	(1.510)	(0.148)	(0.342)
R_squared	0.311	–	0.199	0.199
Rent tenure	0.523^***	1.923	0.848^***	0.799^**
	(0.147)	(1.252)	(0.109)	(0.383)
R_squared	0.230	–	0.450	0.449
Rent free tenure*	0.033^**	−0.074	0.017	−0.075
	(0.012)	(0.098)	(0.017)	(0.085)
R_squared	0.230	–	0.450	0.449
Observations	289	289	289	289
	Low job density, low skill		High job density, low skill
	OLS	IV	OLS	IV
Owner-occupied tenure	0.493^***	−2.245	0.575^***	−0.179
	(0.175)	(3.015)	(0.157)	(0.647)
R_squared	0.137	–	0.195	0.134
Rent tenure	0.440^***	0.529	0.608^***	0.539^*
	(0.115)	(0.960)	(0.142)	(0.319)
R_squared	0.120	0.118	0.171	0.170
Rent free tenure*	−0.020	−0.003	0.007	0.094^*
	(0.014)	(0.086)	(0.027)	(0.052)
R_squared	0.075	0.072	0.072	–
Observations	251	251	252	252

Notes: IV is the instrumental variable based on the historical settlement pattern of different country of origin groups. Both models cluster standard errors at local authority level and they are included in the parentheses. The local authorities are grouped into four categories using two characteristics – employment density captured by the number of jobs over population and a skill index derived from the socioeconomic characteristic of the area. Statistical significance is represented by star symbols where ^* indicates p < 0.1, ^** indicates p < 0.05, ^*** indicates p < 0.01. The first stage coefficients from top left to bottom right are: 0.900^** (4.54), 0.593 (44.33), 1.442 (10.84) and 1.817^*** (10.45). The models are run with the set of socioeconomic controls. *Rent free tenure.

Specifically, an increase in the level of immigration does not affect the level of owner-occupied tenure in any of the four areas. However, regardless of the skill level, the high job density areas see renting rise with around a 0.8% increase in high skill areas and a 0.5% increase in low skill areas. The differentiating feature lies in the form of rent free tenure. In low skill areas, a 1% increase in the stocks of immigrants led to around a 0.1% rise in the form of rent free tenure, and this is not seen in high skill areas. Despite its small magnitude, this could potentially be responsible for the house price reduction in this type of area. This group of the population stands a higher chance of being inactive, or of working for a low wage that could not enable them to rent in the private rental sector. Not only do they themselves have a very low demand for housing, but also this may reduce the incentive for those in contact with them to demand more housing, perhaps through peer pressure. However, this is not empirically tested in this paper.

Conclusion

In conclusion, there is evidence of an around 0.8% decrease in house price in high job density areas with the majority of the occupations in low skillsets. In addition, both native out-migration response and native wage depression could not explain the house price decrease. The authors attempted to seek alternative explanations and discovered that the reduction might be due to an average tenure ‘downgrade’ led by inflows of immigrants. Immigrants are found to have a higher chance of renting freely from existing landlords. In particular, an increase in immigrant stocks equal to 1% of the local initial population has led to a 0.1% increase in free renting in the high density and low skill areas; 0.5% and 0.8% increase in renting in low skill and high skill areas, respectively. Their presence not only prices them out in the rental and owner-occupied sector, but also suppresses housing potentially through peer pressure effect. The authors believe this is one of the causes for house price reduction in this type of area. In fact, if the native flight evidence was not found when stratifying areas according to employment density and socioeconomic profile of workers, it might suggest that segregation is more likely to take place along socioeconomic lines rather than ethnic lines, as the native flight effect disappears after we control for the socioeconomic ranking of the areas. Analogous findings were found in Saiz and Wachter (2011), indicating that black and white residential segregation is more a result of socioeconomic divide rather than a racial divide.

At first glance, the division of areas along the lines of employment density and socioeconomic profile is rather crude. Even within each subgroup, an area could still possess distinct urban structures which lead to further difference in native–immigrant interactions and migration patterns. Therefore, the empirical results found only provide support for some general mechanism that could be at work.

Another point worth noting is that the analysis is done under the framework of native and immigrant interaction. In fact, the two do not have to be treated as mutually exclusive groups, that is, impacts do not need to be only measured on natives’ economic outcomes. If this were relaxed, one could speculate that native migrant workers from the bottom of the income distribution could also densify the area, hence bringing down the overall demand for housing. Of course, this should be subject to empirical test too.

Footnotes

Appendix

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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