Neighbourhood watch: Population forecasting for new housing developments

128 september2005

case studyc a s e s t u d y

Neighbourhood watchPopulation forecasting for new housing developments

In 2003 Paul Garcia was awarded a Royal Statistical Society Campion Fellowship, with a grant of £5000, to carry out a study to try to determine the age profile of residents of new housing developments and explain how that profile changes over time. He describes why he wanted to do the work and how he set about organising the survey.

The need for an age profile

Since housing developments are very small geographi-cal units—much smaller than Census Output Ar-eas—there was no hope of getting any suitable census data. Instead it would be necessary to go out and count people, houses and rooms on a selection of recent hous-ing developments. I could then try to build models of residents’ ages by property type, location and age of de-velopment.

Th e reason I wanted to do this work arose directly out of my job at Hertfordshire County Council (HCC). In June 2001 I escaped from teaching in further educa-tion into a post as a demographer in the Property De-partment of HCC. My brief was to develop a popula-tion forecasting method that could be used to help the council plan its use of property over the next 20 years or so.

Th e method would have to be simple and use only easily available data, in case the forecasts were to be challenged at a public inquiry or in committee. So I constructed a set of linked Excel workbooks to do the job—but that’s another story.

To test the model, I needed a source of good an-nual population counts. Th e only readily available data were the Pupil Level Annual School Censuses, so I got embroiled in pupil forecasting. One of the uses to which pupil forecasting is put is estimating the number of ex-tra pupils that may follow when a new housing devel-

opment is planned. Th e predictions are used to obtain land and fi nancial donations from the developers to cre-ate new schools.

I discovered that most authorities, including HCC, use a set of “pupil yield” rules derived by various means from 1991 census data. Th ey all amount to roughly the same thing: around 15 secondary pupils and 21 primary pupils for every 100 homes constructed. But, as far as I could determine, no one had ever done any studies to see if these rules of thumb matched reality.

It was while I was pondering this that I spotted the advert for the 2002 Campion Fellowship, a couple of weeks before the closing date. In for a penny, in for a pound! I prepared, rather hastily, an application to the Royal Statistical Society and submitted it with moments to spare. I had enlisted the help of the University of Hert-fordshire Business School with the application, since I hoped to pay students to go round knocking on doors.

My plan was to conduct two surveys, about 18 months apart, of a random sample of developments built between 1995 and 2002. Th e two sets of data, covering a variety of ages of development, as well as providing two cross-sectional surveys, would enable me to simulate a longitudinal survey of about 10 years—I hoped. I was delighted to be called for an interview, where the Fellow-ship Committee asked lots of interesting and challeng-ing questions.

Th e upshot was that I wasn’t awarded a fellowship, but I was given some very useful feedback. I tackled the

“My brief was to develop a

population forecasting

method that could be used to help the council plan

its use of property over the next 20

years or so”

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129september2005

case study

main issues raised by the Fellowship Commit-tee: concerns about using two cross-sectional surveys to simulate a longitudinal survey (I found a 1968 paper by Harvey Goldstein that did just that) and my (lack of ) links with lo-cal and central government. I found colleagues in other local authorities and at the Offi ce for National Statistics who expressed an interest in the outcome of the work.

In 2003 I re-applied, was re-interviewed, and this time was awarded a fellowship. So I had £5000 to fund the survey work and 2 years to get it done. Th e offi cial letter confi rm-ing the fellowship took a while to arrive, but I went ahead and produced a workplan and got the university to advertise for a team of willing students.

Planning and training

My fi rst task was to select a sample of devel-opments to survey. Th e Environment Depart-ment at HCC provided me with geo-referenced data on all developments constructed between 1995 and 2002. I divided them into 27 catego-ries using location (rural, outer urban, urban core), size (small (< 25), medium (< 100), large (> 100)), and age (old (1995–1997), me-dium (1998–2000), new 2001–2002).

I chose a 5% random sample from each category, with a minimum of 1 from each category (unless there were no developments in that category). Th e result was a sample of 30 developments with, in total, about 1400 dwellings. Using a GIS system, the Post Offi ce Addresspoint fi le and the Ordnance Survey 1:1250 mapping data, I was able to compile a list of all the addresses in the sample.

Th e plan I hatched was to write to each address about 2 weeks before sending the stu-

dents out, to explain to residents the purpose of the survey and give them a chance to opt out if they wanted to. Th e students were to call at each address to collect data, returning once if the fi rst call didn’t produce a result and leav-ing a blank form (with a reply-paid envelope) if the second call didn’t work either.

Th e amount of paper was enormous. Th ere were fi ve sheets for every dwelling: an explana-tory letter to post, a copy for the student to carry, plus a survey form, a letter to leave after the sec-ond visit and a report form for the student to fi ll in if they had to leave the survey form—plus the reply envelopes. Th e letters for posting had to be printed and posted by an agency but the rest of the documents were printed in-house and collat-ed by me, using my trusty stapler. It took about 2 days! With the paperwork printed, stapled and sorted into piles by development, it was time to get to grips with organising the students.

We recruited a team of 12 volunteers from a group of 23 students who attended an initial briefi ng meeting, at which the purpose and na-ture of the task were explained. At a training

Figure 1. Number of children by dwelling type

“The method would have to be simple and use only easily available data, in case the forecasts were to be challenged at a public inquiry or in committee”

0 100 200 300 400 500 600 700

Converted/SharedHouse

Detached

Purpose Built Flats

Semidetached

Terraced

Number of dwellings

No children 1 child 2 children 3 children 4 children 5 or more children

session I explained my paperwork in what I thought were very simple and clear terms. It is surprising how years of experience as a teacher can be forgotten so quickly! University staff took responsibility for explaining the health and safety aspects of house-to-house work, including clear instructions on not being too outlandish with exposed piercings, tattoos and exotic garb.

Having nodded wisely and confi rmed that they understood everything, the students were organised into fi ve teams (since they possessed fi ve cars between them), allocated to develop-ments, given the piles of paper and sent forth.

Collecting the data

I wasn’t sure precisely when the survey work would start. Th e letters had been sent out on time, and I had had one phone call from a wor-ried woman who didn’t want us to call on her 87-year-old mother since she lived alone in a one-bedroom fl at. Once I had elicited the ad-dress, I was of course able to off er reassurance that we wouldn’t need to bother the pension-er.

I was alerted to the start of data collec-tion by an irate phone call from a resident who claimed that he was being verbally abused by an aggressive young woman. Our exhor-tations as regards dress code hadn’t quite worked, since one man’s outlandish outfi t is another student’s normal daywear. Appar-ently, red bandannas and white tracksuits are normal burglar kit in parts of Hertfordshire,


130 september2005

case study

so the resident had taken a belligerent stance when confronting our student and his female accomplice. Rather than simply back off , the girlfriend had felt obliged to spring to the de-fence of her man, resulting in accusations of racism, and the phone call to me.

Fortunately, the resident was on his way to the airport for a business meeting in Switzer-land, so was keen to get off the phone quickly. Presumably the letter of apology we sent while he was abroad was acceptable, since we haven’t been sued.

Th e only other problem was caused by a slightly over-enthusiastic student who, getting no answer from the doorbell, spotted a resi-dent indoors and knocked on the window. Th e alarmed pensioner rang her daughter, who rang me and berated me soundly for my incredibly poor training programme.

One student became confused by all the paperwork and ended up leaving the report form she was supposed to fi ll in after the second unsuccessful visit and returning the blank survey form to me. Th ere are 18 very confused residents of Hertfordshire who must be wondering why we went to all the

trouble of leaving them a reply-paid enve-lope to return a questionnaire that contained no questions! Indeed, two residents clearly wanted to make the point and returned the envelope with nothing in it.

One team of surveyors fl ew back to Hong Kong for Christmas before they had complet-ed the work. It took the university a long time to retrieve the paperwork, and it took a long time to fi nd someone to fi nish the job.

Our training for the second round of sur-veying will be better!

Some early results

Although we are currently missing 28 sets of data from the “Hong Kong team”, we had a good response rate and the “call twice then leave a form” strategy was a good one.

Th e students knocked on 1364 doors. In 29% of cases someone was at home and co-operative on the fi rst visit, and we received responses from a further 26% on the second call. Responses came in from a further 8% by post (corresponding to a 26% return rate for forms left after the second visit). Residents at

just over 12% of households refused to take part (it would be interesting to go back and ask them why). Th e overall response rate is cur-rently 65.5%. 11 addresses didn’t exist, mostly number 13s.

Th ere are some interesting preliminary re-sults. Th e average number of people per dwell-ing is 2.8, but only 51% of dwellings have chil-dren under 16. Th e average number of children per adult female of childbearing age is only 1.06. Th e median period of residence is 12 months (but with an inter-quartile range of 28 months). Plotting number of bedrooms against number of children gives an R2 value of 0.34. Number of bedrooms against length of time in the property gives an R2 value of 0.40.

I now have to decide how I am going to model the data collected. Suggestions would be most welcome! Th e second survey is sched-uled for September 2005, with the fi nal results due in early 2006.

After teaching mathematics for 17 years, Paul Garcia became a demographer for Hertfordshire County Coun-cil. In his spare time he is working on a thesis in the history of mathematics.


Neighbourhood watch: Population forecasting for new housing developments

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