The historical development of the Swiss rental market – A new price index

Constantinescu, Mihnea; Francke, Marc

doi:10.1016/j.jhe.2013.02.004

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Increasing attention has been paid to track real estate market movements on a detailed level (Constantinescu and Francke, 2013). Granular indices give a better representation of the space and asset market the properties are traded in and are thus more informative.…”

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confidence: 99%

The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

Francke

Minne

2017

J Real Estate Finan Econ

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This paper concerns the estimation of granular property price indices in commercial real estate and residential markets. We specify and apply a repeat sales model with multiple stochastic log price trends having a hierarchical additive structure: One common log price trend and cluster specific log price trends in deviation from the common trend. Moreover, we assume that the error terms potentially have a heavy tailed (t) distribution to effectively deal with outliers. We apply the hierarchical repeat sales model on commercial properties in the Philadelphia/Baltimore region and on residential properties in a small part of Amsterdam. The results show that the hierarchical repeat sales model provides reliable indices on a very detailed level based on a small number of observations. The estimated degrees of freedom for the t-distribution is small, largely rejecting the commonly made assumption of normality of the error term. Keywords Commercial real estate • Residential real estate • Property Price Indices • Thin markets • Bayesian inference • Kalman filter • State space models 1 Introduction Real estate is the largest single class of investment in the world and the biggest source of household wealth in most industrialized countries. Estimating the risk and return

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confidence: 99%

The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

Francke

Minne

2017

J Real Estate Finan Econ

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“…• Switzerland: Nominal house price index is available by Constantinescu and Francke (2013). Among the various indices the authors estimate, we focus on the local linear trend (LLT) index.…”

Section: A33 the Riskiness Of Housing -Detailsmentioning

confidence: 99%

Very Long-Run Discount Rates

Giglio¹,

Maggiori²,

Stroebel³

2014

gwp

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We provide direct estimates of how agents trade off immediate costs and uncertain future benefits that occur in the very long run, 100 or more years away. We exploit a unique feature of housing markets in the U.K. and Singapore, where residential property ownership takes the form of either leaseholds or freeholds. Leaseholds are temporary, pre-paid, and tradable ownership contracts with maturities between 99 and 999 years, while freeholds are perpetual ownership contracts. The difference between leasehold and freehold prices reflects the present value of perpetual rental income starting at leasehold expiry, and is thus informative about very long-run discount rates. We estimate the price discounts for varying leasehold maturities compared to freeholds and extremely long-run leaseholds via hedonic regressions using proprietary datasets of the universe of transactions in each country. Agents discount very long-run cash flows at low rates, assigning high present values to cash flows hundreds of years in the future. For example, 100-year leaseholds are valued at more than 10% less than otherwise identical freeholds, implying discount rates below 2.6% for 100-year claims. Given the riskiness of rents, this suggests that both long-run riskfree discount rates and long-run risk premia are low. We show how the estimated very long-run discount rates are informative for climate change policy.

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Estimating Census Tract House Price Indexes: A New Spatial Dynamic Factor Approach

Francke

Rolheiser

Minne

2023

J Real Estate Finan Econ

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Geographically and temporally granular housing price indexes are difficult to construct. Data sparseness, in particular, is a limiting factor in their construction. A novel application of a spatial dynamic factor model allows for the construction of census tract level indexes on a quarterly basis while accommodating sparse data. Specifically, we augment the repeat sales model with a spatial dynamic factor model where loadings on latent trends are allowed to follow a spatial random walk thus capturing useful information from similar neighboring markets. The resulting indexes display less noise than similarly constructed non-spatial indexes and replicate indexes from the traditional repeat sales model in tracts where sufficient numbers of repeat sales pairs are available. The granularity and frequency of our indexes is highly useful for policymakers, homeowners, banks and investors.

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The historical development of the Swiss rental market – A new price index

Cited by 5 publications

References 28 publications

The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices

Very Long-Run Discount Rates

Estimating Census Tract House Price Indexes: A New Spatial Dynamic Factor Approach

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