Presentation on theme: "Maurizio Grilli & Richard Barkham June 2012 City rents in a global context."— Presentation transcript:
Maurizio Grilli & Richard Barkham June 2012 City rents in a global context
Aim of the research ■ Most models looking at the determinants of rental change generally aim at explaining rental changes in the short-run. Models can be macro (mostly TS) or micro (mostly cross- sectional). ■ We aim at establishing an intuitive hierarchy of rental values across markets. ■ The markets we analyse here are urban conurbations across the globe. We believe that property investment is essentially city- driven (rather than country-driven) and, as a result of on-going urban growth, it is vital to be able to pick those cities which will outperform.
The rationale ■ By understanding the drivers behind rental values, an investor may acquire assets in the markets that are currently under-rented and thereby out-perform competitors. Vice versa investors can avoid the high risks associated with over-rented markets. ■ By taking a long-run perspective, subject to accurate forecasts of the drivers of rental values, an investor may deploy capital in the markets that will deliver the highest capital value uplift.
The cities ■ Half of the world’s population live in cities and these generate more than 80% of global GDP. The top 600 cities (equivalent to 20% of the world’s population) deliver 60% of global GDP. In 2030, the top cities will still provide most of total GDP, but the names of those cities will be different. ■ A successful city will generally have most, if not all, of the following features: – Large size, in terms of population, GDP and real estate stock; – A strong and diversified economy including advanced business services; – A well-educated workforce; – High level of connectivity; – Low levels of crime; – A good transport system; – Good entertainment and cultural offer; – A general sense of vibrancy and innovation; – High standard of liveability; – A cosmopolitan feeling; – A responsible environmental policy.
The data ■ By drawing on the Grosvenor in-house database we were able to collect office and retail rental data for 140 cities. This was supplemented with residential rental data for more than 110 cities. ■ The explanatory variables found to be most important are as follows: – GDP; – Connectivity; – Quality of life; – Population density; – Planning constraints.
Total GDP in the top 30 cities Source: PWC, Global Insight, local sources, Grosvenor Research, 2012 GDP (US$ bn)
Relation between rents and GDP Source: PWC, Global Insight, local sources, Grosvenor Research, 2012 Office rents (US$/sqm/year) GDP Residential rents (US$/sqm/year) Retail rents (US$/sqm/year)
Cities ranked according to connectivity Source: GAWC, University of Loughborough, Grosvenor Research, 2012 Connectivity coefficient (max=1)
Relation between rents and connectivity Source: GAWC, University of Loughborough, Grosvenor Research, 2012 Office rents (US$/sqm/year) Connectivity Residential rents (US$/sqm/year) Retail rents (US$/sqm/year)
Cities ranked by quality of life Source: EIU, Grosvenor Research, 2012 Quality of life (100= ideal)
Relation between rents and quality of life Source: EIU, Grosvenor Research, 2012 Office rents (US$/sqm/year) Quality of life Residential rents (US$/sqm/year) Retail rents (US$/sqm/year)
Cities ranked by population density Source: Demographia, Grosvenor Research, 2012 Population density – people per sq km
Relation between rents and population density Source: Demographia, Grosvenor Research, 2012 Office rents (US$/sqm/year) Population density Residential rents (US$/sqm/year) Retail rents (US$/sqm/year)
Real rental levels in the UK Source: CBRE, ONS, Grosvenor Research, 2012 Index 1972=100
The office model Source: Grosvenor Research, 2012 Dependent Variable: Office rental values Included observations: 72 after adjustments VariableCoefficientt-StatisticProb. constant254.92.61% GDP0.43.60% connectivity427.83.10% population density0.13.60% vacancy rate-11.5-2.05% dummy-169.1-2.33% R-squared0.6
Offices: over and under-renting Source: Grosvenor Research, 2012 Degree of over and under-renting % over -rented under -rented
The retail model Source: Grosvenor Research, 2012 Dependent Variable: Retail rental values Included observations: 62 after adjustments VariableCoefficientt-StatisticProb. constant-9771.4-3.80.0 GDP188.8.131.52 connectivity53184.108.40.206 liveability220.127.116.11 population density0.23.10.0 EU dummy1318.104.22.168 R-squared0.6
Retail: over and under-renting Source: Grosvenor Research, 2012 Degree of over and under-renting % over -rented under -rented
The residential model Source: Grosvenor Research, 2012 Dependent Variable: Residential rental values Included observations: 74 after adjustments VariableCoefficientt-StatisticProb. constant-398.4-0.50.6 GDP22.214.171.124 liveability126.96.36.199 population density0.01.50.1 AM dummy-634.2-3.00.0 R-squared0.6
Residential: over and under-renting Source: Grosvenor Research, 2012 Degree of over and under-renting % over -renting under -renting
The importance of different variables for different sectors Source: Grosvenor Research, 2012 GDPConnectivity Quality of life Population density Vacancy rate Office rentsstrong weakmedium Retail rentsstrong medium * Residential rentsstrong weak * ] ] * Due to data availability issues, the vacancy rate could be used only for offices.
Conclusions ■ Demand, as represented by GDP, and supply as, proxied by long term vacancy, are key determinants of real estate values as theory would suggest and numerous studies attest. ■ Population density is generally associated with higher rental values. It is probable that this represents both cause and effect. Higher rents cause land to be used more intensively, but output is itself a positive function of density due to agglomeration economies. ■ The positive association between rents and livability scores, after controlling for other factors, shows that value and presumably tax revenues, accrue to well managed cities. ■ One of the most interesting findings of the study is the relationship between connectivity, which describes the economic ‘influence’ or ‘reach’ of a city, and rents. This is evidence that real estate outcomes at the city level are increasingly being driven by the forces of globalisation.
Your consent to our cookies if you continue to use this website.