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New dwelling completions

This publication outlines the background, methodology, and results of efforts to improve housing completion statistics in Ireland. It addresses issues with timeliness, coverage, quality, and compliance, and highlights the involvement of the CSO in reviewing and recommending improvements to data. The methodology includes utilizing available data sources, such as ESB connections and census data, to estimate and classify new dwellings accurately. The publication also discusses future steps, including data linkage and accessing additional administrative data sources.

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New dwelling completions

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  1. New dwelling completions National Statistics Board 7th November 2018

  2. Outline Background to publication Methodology Results Next steps

  3. Background to publication

  4. Recovery of construction sector

  5. Debate around housing completion statistics • Most datasets are not timely • Coverage of new dwellings • Quality and compliance issues • Lack of unique identifiers

  6. Why are the CSO involved ? • DHPLG - Housing Analytics Group (HAG) set up in 2017 • Review housing data and identify gaps/deficiencies • Make recommendations to improve data • Evaluate quality/appropriateness of ESB data • National Data Infrastructure (NDI) • Common identifiers to link data across Public Sector • Eircode – Unique identifier for linking address

  7. Housing Completions – The Task • ESB connections published by DHPLG as proxy for housing completions, series from 1970s • Evaluate quality and appropriateness of ESB data as a measure of housing completions • Analyse other available data sources • Develop methodology utilising relevant data sources to estimate housing completions

  8. ESB Connections – Goodness of Fit ? • Administrative system designed for recording connections to ESB network and not counting new houses • Over coverage • Reconnections after two years • Non-dwelling/non-residential • Previously completed dwellings in Unfinished/Ghost Estates (UFHDs) • No significant under coverage • Student accommodation not in ESB domestic connections dataset

  9. Methodology

  10. Census of Population used as benchmark Census = count of all houses in State at point in time Match ESB connections after Census Night 2011 to Census 2011 and 2016 datasets Requires Eircode asunique identifier on ESB and Census data Address string matching

  11. Matching process to obtain Eircode Assign Eircode to ESB Connections to allow linking to Census 84,500 connections between 2011-2017: 45% Eircode match Remove 2017 and for Intercensal Period : 61% Eircode match

  12. Methodology: Non-dwellings & Reconnections • Not all ESB connections are new dwellings but all new dwellings in ESB connections • Engagement with ESBN and extraction of additional connections data under Statistics Act allows • Identification of Non-dwellings • Identification of Reconnections

  13. Methodology: Unfinished Housing Developments • Dwellings enumerated in Census 2011 are classified as UFHD • Dwellings not enumerated in Census 2011 are classified as New • Derive rules based on characteristics of these records and used to classify data that cannot be matched to Census • Accuracy is increased by linking to BER and Geodirectory and other sources when available

  14. Classification Rules • Recursive Partitioning used for simple baseline rules • Characteristics used for classification • Time lag between Authorised and Energised Dates • Type of Dwelling • Misclassification Rate: 17.5%

  15. Improving Accuracy New Dwellings misclassified as “Unfinished” “Unfinished” Dwellings misclassified as New Link to BER using MPRN or Geodirectory using Eircode to get construction date and reclassify As more data sources can be linked classification can be further improved

  16. Results

  17. ESB connections on average 1,000 higher than new dwelling completions

  18. Unfinished dwellings gradually declining

  19. Next steps

  20. Next steps • Q3 release on Friday, November 9th • Social Housing in Ireland • Data linkage using administrative data sources • Access to HAP data and social housing waiting lists • Profile of people, e.g. socio-demographics, income, type of tenure, length of access

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