Loan-to-Value Regulations on Mortgages and the Use and Refinancing of Unsecured

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The end of Oslo's rent control: Impact on rent level

Rent control provides an opportunity to study policymakers' ability to control prices on a large scale, in a sector that has significant welfare effects. We investigate the removal of rent control in the Norwegian capital Oslo in 1982 using a long dataset, with observations from 1970 to 2011. This allows us to exclude business-cycle fluctuations and ensure that the market and rent level are no longer affected by the rent control, and that rent has reached a new long-term equilibrium. We do not find that the removal of the rent control led to an increase in private rents in Oslo. It would appear that landlords' asking rent was equal to the market clearing rent in both the period with rent control (1970–1981) and that without rent control (1982–2011). The rent control in Oslo did not have the desired welfare distribution effects

The removal of rent control and its impact on search and mismatching costs: evidence from Oslo

The removal of the Norwegian rent control in 1982 created a natural experiment that enabled us to investigate whether rent control affected the search and matching process in the private residential rental market in the Norwegian capital, Oslo. We collected and analysed data on ‘housing for rent’, ‘housing wanted’ and ‘housing exchange-wanted’ advertisements in Oslo covering a period from 1970 to 2008. We concluded that the use of newspaper listing services by potential tenants and landlords changed after the rent control removal. Our results indicate that it is more costly, in time and money, for a potential tenant to search for and to find a home under rent control. Moreover, our results indicate that rent control increases the probability of and the distance from the ideal dwelling, in size, standard and location, a potential tenant have to settle for

What is a housing bubble?

The aim of this paper is to look at the developments in previous housing price cycles to improve our understanding, and to create a descriptive definition, of what a house price bubble is and to lay the groundwork for future research. A descriptive definition opens a lot of research opportunities with empirical studies of large datasets, such as: How costly are housing price bobbles? Is there a pattern associated with bubbles? Which indicators can be used to identify bubbles? We find the peaks and troughs and study the price movements around these points using two datasets with housing price data. We use one quarterly dataset from 1970 to 2015 for 20 OECD countries, and one yearly set with 6 countries and 2 cities, where 6 of the data series go back to the 1800s. A large housing price bubble has a dramatic increase in real prices, at least 50% during a five-year period or 35% during a three-year period, followed by an immediate dramatic fall in the prices of at least 35%. A small bubble has a dramatic increase in real prices, at least 35% during a five-year period or 20% during a three-year period, followed by an immediate dramatic fall in the prices of at least 20%

The impact of historic preservation policies on housing values

Historic preservation dwellings offer qualities that benefit both owners and society. At the same time, preservation policies might include some costs and restrictions. Although many studies have aimed to assess the impact of historic preservation on housing values, this study, to our knowledge, is the first to investigate whether the historic preservation premium is due to the changed juridical status (a policy effect), or the qualities observed by the buyers that are unobserved in the model. By using a unique data set that combines data of preserved historic dwellings in Oslo, Norway, and data from the housing market from 1990 to 2017, we study sales prices for the same dwellings both before and after historic preservation. The higher prices of preserved historic dwellings seem to be caused by qualities in the dwellings that correlate with the forthcoming historic preservation, and not by the policy itself

Combining Property Price Predictions from Repeat Sales and Spatially Enhanced Hedonic Regressions

Hedonic regression and repeat sales are commonly used methods in real estate analysis. While the merits of combining these models when constructing house price indices are well documented, research on the utility of adopting the same approach for residential property valuation has not been conducted to date. Specifically, house value estimates were obtained by combining predictions from repeat sales and various hedonic regression specifications, which were enhanced to account for spatial effects. Three of these enhancements—regression kriging, mixed regressive-spatial autoregressive, and geographically weighted regression—are widely utilized spatial econometric models. However, a fourth augmentation, which addresses systematic residual patterns in regressions with district indicator variables and the presence of outliers in housing data, was also proposed. The resulting models were applied to a dataset containing 16,417 real estate transactions in Oslo, Norway, revealing that when the repeat sales approach is included, it reduces the median absolute percentage error of solely hedonic models by 6.8–9.5%, where greater improvements are associated with less accurate spatial enhancements. These improvements can be attributed to the inclusion of both spatial and non-spatial information inherent in previous sales prices. While the former has limited utility for well-specified spatial models, the non-spatial information that is implicit in previous sales prices likely captures otherwise difficult to observe phenomena, potentially making its contribution highly valuable in automated valuation models