Eres : Digital Library : Works

Paper eres2012_204:
Tracking real estate cycles

id eres2012_204
authors Shreyas Phadnis
year 2012
title Tracking real estate cycles
source 19th Annual European Real Estate Society Conference in Edinburgh, Scotland
summary Property markets move in cycles, and tracking them is critical for an investor. The proposed framework, called Betanalytics, uses statistical tools to anticipate changes in property trends. It generates lead signals giving time buffers to re-configure a portfolio for a downturn or an upswing. Coverage: Back testing results, currently focusing on UK and European Office sector, show a success rate of 80% plus consistently across 21 cities. Nine variables across each city complete the framework. These include: Sales Prices, Rental Rates, Yields, Spreads between Government Bonds and Property yields, GDP, Production, Unemployment, Inflation, Interest Rates. What does it do? For London office market alone, from Q2-1984 to Q3-2011 an investor would have captured 87-96% of the rallies and avoided 69-95% of the downtime. Sales price chart alone generates a hit rate of 86%. Combining signals with other variables improves this. Note: London is probably the trickiest market to analyse because it attracts foreign capital. Limitations: The computer program is only an enabler, not the product. Interpretation is key. The framework does not forecast in a conventional sense (using regression); it primarily helps understand the risk-reward ratio within market cycles. It does not guarantee success, but it signals high probability set-ups based on a back-tested model. Key findings: Rentals lag Sales: Sales are speculative and Rentals are need-based. Rentals are less volatile than Sales: Sales are transaction-based, hence momentary. Rentals are contractual, hence sticky. Macro factors: GDP, Inflation, Interest Rates and Production hold limited correlation with property cycles. They are useful for general overview. Unemployment: Employment actually tends to be counter-cyclical to Property. High unemployment: distressed sellers, attractive yields time to buy. Low unemployment: high inflation, compressed yields time to sell (or hedge). Bonds and Property Yields: the correlation exists, but not consistent across 21 cities.
series ERES:conference
type normal paper
discussion No discussions. Post discussion ...
session Parallel Session H1
last changed 2014/10/21 21:51
HOMELOGIN (you are user _anon_258196 from group guest) Powered by SciX Open Publishing Services 1.002