Repeat Sales House Price Index Methodology

Nagaraja, Chaitra H.; Brown, Lawrence D.; Wächter, Susan M.

doi:10.1080/10835547.2014.12090375

Cited by 34 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this method, matching pairs of sales of the same objects are gathered and used to calculate price differences. One of the assumptions here is that the compared objects are identical (Nagaraja, Brown, & Wachter, 2014). In case of actual sales, the period between two sales of the same object can cover a long range (real estate is usually not a short term purchase).…”

Section: Figure 6 -Stepwise Demarcation Of Real Estate Spe Share Dealsmentioning

confidence: 99%

The Effect of Real Estate Share Deals on Commercial Property Price Indicators

Ishaak¹,

Schie²,

Haan³

2021

28th Annual European Real Estate Society Conference

View full text Add to dashboard Cite

Section: Figure 6 -Stepwise Demarcation Of Real Estate Spe Share Dealsmentioning

confidence: 99%

The Effect of Real Estate Share Deals on Commercial Property Price Indicators

Ishaak¹,

Schie²,

Haan³

2021

28th Annual European Real Estate Society Conference

View full text Add to dashboard Cite

“…Perhaps the most significant differences are the estimation methods used to construct each index: ZHVI relies on AVMs; whereas S&P C-S and FHFA are constructed from weighted least squares regressions. Overviews of the FHFA and S&P C-S methodologies are presented in Appendices B and C. For a complete overview of HPI methodologies, we refer the reader to Nagaraja et al, 15,16 who chronicle the statistical development of HPIs from the original repeat sales approach of Bailey et al 2 to modern statistical methods of Nagaraja et al 7 Differences in the HPIs are not limited to estimation methods. The underlying data sources differ as well.…”

Section: Comparisonmentioning

confidence: 99%

“…The quadratic component was first introduced to the literature by Abraham and Schauman, 30 who explained " … we do not believe that the variance in price of individual properties around the index will increase linearly forever." Nagaraja et al 15 note that this quadratic variance component in the model has statistical implications when a single home transacts more than twice, and it results in an estimate βt that is unbiased but not the minimum variance estimator.…”

Section: Appendix B Fhfamentioning

confidence: 99%

“…Overviews of the FHFA and S&P C‐S methodologies are presented in Appendices B and C. For a complete overview of HPI methodologies, we refer the reader to Nagaraja et al, 15,16 who chronicle the statistical development of HPIs from the original repeat sales approach of Bailey et al 2 to modern statistical methods of Nagaraja et al 7 …”

Section: Zillow Home Value Indexmentioning

confidence: 99%

See 1 more Smart Citation

Learning low‐dimensional structure in house price indices

Glynn

2021

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

House price indices (HPIs) are statistical measures of real estate price dynamics in defined geographic regions over defined periods of time. HPIs are important metrics that help policymakers, mortgage lenders, real estate investors, and bank regulators monitor market conditions and manage risk. HPIs that are local, reliable, and timely are essential in understanding connections between housing markets and the broader economy. In this article, we examine the algorithmic construction of Zillow's Home Value Index (ZHVI), an HPI built on black box machine learning algorithms. To provide deeper statistical insight into ZHVI than afforded by its black box construction, we develop a Bayesian generative meta-model that approximates the black box construction of ZHVI series in 100 metropolitan areas (metros). Each ZHVI series is modeled with a global trend, a finite mixture of Gaussian processes, and a local component. We find that there are three shared dynamic patterns across the 100 markets in our analysis, and we utilize this shared latent structure to forecast ZHVI in each metro 12 months ahead. Our clustering strategy has two advantages: (i) it allows us to construct composite HPIs where member metros are learned from the data rather than predetermined; and (ii) it allows us to estimate the relative contributions of cluster-level and metro-specific components to a metro's ZHVI, providing a novel statistical attribution of real estate market dynamics.

show abstract

“…The advantage of the Case-Shiller method is that it allows for time-weighting of transactions, so we choose this method. There are other approaches to constructing repeat sales price indices Nagaraja, Brown and Wachter (2014). compare five methods and conclude that all indices are adequate.5 Because SAND was introduced in August 2020, transactions prior to SAND's existence are excluded for analyses denominated in SAND.6 DAI is primarily used to mint LAND, so there is little variation left for secondary transactions.…”

mentioning

confidence: 99%