The Changing Face of the UK
High Street: Forecasting the
future for 2020
Professor Cathy Parker
@placemanagement
#HSUK2020
Our partner HSUK2020 towns:
Alsager, Altrincham, Ballymena,
Barnsley, Bristol (St George),
Congleton, Holmfirth, Market
Rasen, Morley and Wrexham
100,000
!
An academic study. Why bother?
People do not want to go into six different shops for six
different articles; they prefer to buy the lot in one shop.
The American Grocer, 1892
For better or worse this distributive revolution is carrying
us away from shopkeeping to mass distribution
McNair, 1931
Change in retailer location 2000-11
Out of town
up 50
million
square feet
Town centre
down 46
million
square feet
Department for Transport 2011
Online share of home retailing
2014
Poland
France
Europe av
Germany
UK
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%
Centre for Retail Research 2013
Online retailing
16% pa
£52 bn in 2015
M-retailing
62% this year
£7.92 bn
Centre for Retail Research 2013
Town centre share of retail spend
2000
49.4%
2011
42.2%
2014
39.8%
Parliament 2014
The response
•
•
•
•
•
•
•
Vital & Viable Town Centres
Planning Policy Guidance/Statements
Business Improvement Districts
High Street Britain 2015
The Portas Review
Understanding High Street Performance
Future High Streets Forum
Place management
Nature of place management
schemes
IPM 2009
What causes High Street Change?
What influence do individual locations have?
10000 studies found
2345 after clean up
923 ‘retail’ highlighted
253 town centre/high st
173 studies reviewed
Mostly from UK and Europe
Focus of data
City Centre
Town Centre
High Street
Neighbourhood Centre
District Centre
Suburban Centre
Out of town Centre
166 factors influence performance
And if 166 factors were not enough…..
• Partner towns identified 50 additional factors
that influence the High Street
• 33 additional studies reviewed
• 201 factors finally identified, but:
– how much influence does each one have?
– what should towns be focussing on?
The Delphi Technique
The Delphi method is unique in its method of
eliciting and refining group judgement as it is based
on the notion that a group of experts is better than
one expert when exact knowledge is not available.
(Paliwoda, 1983).
22 Experts participated
Practitioner
Academic
Major retailer
Manchester Metropolitan
University
Shopping centres owner
University of Leicester
Urban consultant
University of Dundee
Retail letting agency
University of Ulster
Urban policy group
Oxford University
Trade association
University of Manchester
Professional body
University of Liverpool
University of Portsmouth
University of Loughborough
Consensus reached on
1. How much influence each factor
has on the vitality and viability of
the High Street
2. How much control a location has
over the factor
4
Amount of influence location has over factor
Child-minding centre
Opening hours
3.5
Deliveries
Leadership
3
2.5
2
Political climate
Methods of classification
1.5
Spatial structure
Location
1
2
2.5
3
3.5
4
4.5
Amount of influence factor has over vitality and viability
5
Amount of influence location has over
factor
4
Child-minding centre
3.5
Opening hours
Not worth it!
Get on with it!
Deliveries
Leadership
3
2.5
2
Political climate
Methods of classification
1.5
Forget it!
Live with it!
Spatial structure
Location
1
2
2.5
3
3.5
4
4.5
5
Amount of influence factor has over vitality and viability
Top 25 priorities
3.900
APPEARANCE
How much town can influence factor
3.700
3.500
3.300
NECESSITIES
PLACE MARKETING
EXPERIENCE
PLACE ASSURANCE
NETWORKS &
ACTIVITY HOURS
ENTERTAINMENT AND
PARTNERSHIPS
MANAGEMENT
LEISURE
WITH COUNCIL
RECREATIONAL
MERCHANDISE
Anchor stores
SPACE
VISION&STRATEGY
RETAILERS
WALKING
DIVERSITY
ADAPTABILITY
Chain vs independent
Safety/crime
3.100
LIVEABLE
ATTRACTIVENESS
Comparison/convenience
Barriers to Entry
2.900
ACCESSIBLE
2.700
3.300
3.500
3.700
3.900
4.100
4.300
How much factor influences vitality and viability
4.500
4.700
Forecasting the future for your High
Street
The HSUK2020 Model of High Street
Change
Spatial
Macro
Meso
Micro
Data
•
•
•
•
Footfall supplied by Springboard
62 UK towns and cities
30 months of footfall (2012-2014)
563,828,709 people counted!
Spatial factors
Location
Distance to centre
Size/Type of town
Spatial structure
Towns can’t do anything
about these factors!
Towns in NW & NE have 10% less footfall than expected
Macro factors
Economy
Consumer trends
Business rates
Ageing population
Technology
Retail planning policy
Towns can’t change
these
on their own
25% decline in footfall in last 3 years (internet shopping and recession)
We predict 21% decline by 2020
Meso factors
Barriers to entry
Competition (other towns)
Comparison/Convenience
Out of town shopping
Tenant variety
Vacancy rates
Towns interact with
these/have some
Influence
A stronger or OOT centre within 10 miles account for 30% less footfall
Micro factors
Cleanliness
Visual appearance
Networking
Opening Hours
Attractions
Centre Marketing
Amenities
Car-parking
Entertainment
Leadership
Our model predicts that micro factors explain up to 37% of variation in footfall
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Delphi
Average
RESULTS from #HSUK2020 towns
Spatial
Macro
Meso
Micro
Partnerships
have 64 % of
potential
influence they
could have
(losing 13.3%
footfall)
“..lively, diverse,
intense cities
contain the
seeds of their
own
regeneration…”