Boligprisene har steget mye i urbane områder de siste to tiårene -og betydelig mer enn disponibel inntekt. Denne artikkelen beregner en kjøpekraftsindeks basert på maksimalt låneopptak for representative single førstegangskjøpere og transaksjonspriser i 43 norske kommuner mellom 2003 og 2019. Dette kalles «førstegangskjøperes kjøpekraftsindeks» (FKI). Denne metoden gir flere gevinster sammenlignet med enklere mål som ofte benyttes, som pris-inntekts rater, og egner seg samtidig til regelmessige oppdateringer. Beregningene sammenlignes med utviklingen i faktiske førstegangskjøp, og kan tyde på at mange unge strekker seg langt utover grensene for det ens egen økonomi tilsier. Mens en typisk singel førstegangskjøper ville ha råd til 29 % av solgte boliger i de seks største norske byene i 2010, er det tilsvarende tallet 7 % av solgte boliger i 2019. 1 En prosyklisk utlånspraksis øker maksimalt låneopptak under boom-perioder og svekker maksimalt låneopptak under bust-perioder. Resultatene indikerer at det ikke kun er i Oslo hvor barrierene for boligeierskap har økt, men at den geografiske spredningen er større. Samtidig illustreres de store regionale forskjellene.

We rely on novel textual analysis of real estate listings and identify renovated dwellings in a dataset of Norwegian transactions to estimate the renovation premium in an urban housing market. The renovation premium is estimated in a hedonic framework by classical regression approaches and a random forest algorithm. The strength of the latter is that it allows for a more complex interplay between the renovation premium and explanatory variables. We estimate a significant positive renovation premium of 5–7 percent for renovated dwellings and a negative premium of 9–10 percent for unmaintained/neglected dwellings. These averages mask significant variations in these premiums over time, particularly, a counter-cyclical effect. Omitting renovation information also has implications for estimated short-term house price growth. Unmaintained dwellings tend to transact more in the fourth quarter, indicating that parts of the seasonal price variation reported in the literature are due to compositional variation with respect to renovation. This composition effect bias price movement estimates downward, if uncontrolled for, as unmaintained dwellings transact at significantly lower prices.

We rely on novel textual analysis of real estate listings and identify renovated dwellings in a data set of Norwegian transactions to estimate the renovation premium in an urban housing market. The renovation premium is estimated by classical regression approaches as well as random forests models. The strength of the latter is that it allows for a more complex interplay between the renovation premium and explanatory variables. We find a significant positive renovation premium of 5-7 percent for renovated dwellings and a negative premium of 9-10 percent for unmaintained/neglected dwellings. These averages mask significant variation in these premiums over time. In particular, there is a counter-cyclical effect. In a hedonic price model, omitting renovation has implications for estimated short-term house price growth. We also find that unmaintained dwellings tend to transact more in the fourth quarter, indicating that parts of seasonal price variation reported in the literature are due to compositional variation with respect to renovation. This composition effect tends to bias price movement estimates downward, if uncontrolled for, as unmaintained dwellings tend to transact at a significantly lower price.

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