Driving value through data

HACT provides a range of cutting edge data, analytic and research services to housing providers:

  • Data audit – helping ensure you are collecting and using data in ways that generate maximum value for your business.
  • Data analysis and visualisation – using the latest data analytic approaches to help you understand what the data in your business (and beyond) is telling you about your customers, your assets and your business processes.
  • Data redesign and data governance – helping you build approaches in your business that maximise the value and insight generated by the data you collect and store – drawing on best practice from across public and private sectors.
  • Predictive modelling – using cutting edge data science and Big Data tools, such as neural network modelling and cluster analysis to help deliver new and predictive insights around critical business areas such as customer churn and arrears propensity.
  • Maximising impact – design and analysis of Randomised Controlled Trials to help robustly establish the impact of major business initiatives and identify the most effective interventions, including tenancy sustainment, arrears management, service design and customer satisfaction.

Our Expertise

HACT has substantial experience of delivering data-driven project to housing and related sectors. Recent projects include:

  • Housing Big Data – HACT worked with 18 housing providers to bring together data to investigate data quality and the potential to perform predictive analytics focussing on rent arrears and property repairs.
  • Community Investment and the Bottom Line – the impact community investment schemes have upon the bottom line costs incurred to housing associations was investigated by amassing financial, demographic and intervention datasets.
  • Council Housing Stock Schema – HACT was contracted by a large City Council to design a bespoke schema to store housing stock records.
  • Asset data modelling and visualisation – HACT conducted proof-of-concept modelling and visualisation of a variety of asset data for the asset directors of a group of large housing associations, including examining potential efficiency savings available from co-location of service.
  • Commissioned analysis of policy data – HACT was subcontracted by academics at a Russell Group university to conduct quantitative analysis of data on a recent housing legislative change. HACT’s roles included novel data collection as well as analysis of existing large national datasets.
  • Randomised Controlled Trials into tenancy sustainment – HACT is conducting three RCTs with G15 landlords to examine the effectiveness of various services and communication methods at supporting tenants in order to reduce arrears and minimize tenancy failure.

Data Science Personnel

Christina Knudsen, Data Scientist: Christina joined HACT in June 2015. After completing her Psychology degree at Cardiff University, she went on to read Data Science at Royal Holloway, University of London and is in the process of gaining her MSc. Christina has an interest in the public sector and has previously worked for the NHS during her studies. The opportunity to use her analytic and programming skills while working at HACT is something Christina is greatly looking forward to. 


HACT is happy to discuss and any proposals for quantitative data analysis in the sector, and will be happy to help develop your ideas and provide a consultancy service based around your needs.

Please contact jim.vine@hact.org.uk to discuss further.

Related blog posts

This four-part introductory series into the initial 'Housing Big Data' project was written for the HACT blog and can be accessed through the links below.

Part 1: What is Big Data?

In part one of this short introductory series, Jim Vine explains what is meant by Big Data and how this relates to the world of housing.

Part 2 approaches to delivery

In part two of this short introductory series Jim Vine explains how Big Data might be analysed in the housing sector.

Part 3: machine learning

In part three of this short introductory series, Jim Vine outlines some of the machine learning techniques that are likely to be deployed in analysing housing’s Big Data.

Part 4: potential benefits

In part four of this short introductory series Jim Vine delves into the ‘why’ of Housing Big Data.