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THE NATURAL VACANCY RATE: AN ALTERNATIVE RENTAL APARTMENT MARKET INDICATOR
by
Adam Mark Szymczak
A Thesis Submitted to the College of Graduate Studies and Research
through the Department of Geography in Partial Fulfillrnent of the Requirements for
the Degree of Master of Arts at the University of Windsor
Wndsor, Ontario, Canada
0 1 998 Adam Mark Szymczak
National Libtary I*I of Canada Bibliothéque nationale du Canada
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ABSTRACT
The balanced market vacancy r a t e i s a market i n d i c a t o r
u t i l i z e d by housing ana l ys t s t o determine one aspect o f t h e
p r i v a t e r e n t a l apartment market. The a c t o f comparing t h e
observed vacancy r a t e t o t he balanced r a t e supposedly revea l s t o
what degree a market i s over- o r under-suppl ied. However, t h e
balanced market vacancy r a t e i s assumed t o have t h e same va lue
f o r a17 c i t i e s , regard less o f market, demographic o r l e g i s l a t i v e
f a c t o r s . An a l t e r n a t i v e market i n d i c a t o r i s t h e na tu ra l vacancy
r a t e as def ined by Rosen and Smith (1983). The n a t u r a l vacancy
r a t e i s a r a t e a t wh'ch change i n r e a l r e n t s i s zero. I t i s
assumed t h a t f a c t o r s such as government r e g u l a t i o n s have, over
t ime, a f f e c t e d the market. Th is suggests t h a t t h e n a t u r a l vacancy
r a t e i s p re fe rab le t o t h e balanced market vacancy r a t e as a
market i n d i c a t o r because i t considers va r i ous socio-economic and
demographi c v a r i ab1 es.
A pooled data 1 i n e a r regress ion a n a l y s i s was used t o
eva iuate the usefu lness o f t h e n a t u r a l vacancy r a t e mode1 as a
market i ndi c a t o r . Addi t i onal 1 y, t h e components o r determi nants of
t h e n a t u r a l vacancy r a t e were examined us ing a m u l t i p l e
regress i on. Thi s s tudy found t h a t a f u n c t i onal r e l a t i o n s h i p
e x i s t e d between t h e percen t change i n r e n t and d i f f e r e n c e between
t h e observed vacancy r a t e and t h e n a t u r a l vacancy r a t e . The
r e l a t i o n s h i p between t h e components o f t h e n a t u r a l vacancy r a t e
and t h e l e v e l o f the n a t u r a l vacancy r a t e was uncer ta in . Though
t h e c o r r e l a t i o n c o e f f i c i e n t i ndicated a s t r o n g and d i r e c t
r e l a t i o n s h i p , cau t i on should be used i n i n t e r p r e t i n g the
regress ion s t a t i s t i c s s ince the sample s i r e w a s smal l .
iii
I would l i k e t o take t h i s oppor tun i ty t o extend m y g ra t i tude
t o D r . A . Vak i l , rny primary advisor , and Doug Caruso, my second
reader, f o r t h e i r feedback and guidance. 1 would a l so 1 i ke t o
o f f e r rny apprec ia t ion t o D r . P. Angl in, m y ex te rna l examiner, f o r
h i s val uable comments and feedback, espec ia l l y i n regards t o the
mode1 and t e s t i n g .
Speci a l thanks t o t he "are you done yet " crew who helped me
through t h i s ordeal ( i n no p a r t i c u l a r o rder ) : Bernadette Bruette,
Heather Jablonski , Jim Yanchula, Phi1 8 A l l i s o n Brand, C u r t i s
Keabl e, Brendan 8 D i anne Kennal ey, Chri s Matthews, Isabel Qui roz
de O1 ivares, Chr is & L isa Muggridge, John Pucher 8 Cindy Schultz,
Candi ce Sarnecki , James Stummer, Bev 8 Gerry Muggri dge, Pam
Wainwright & Steve W i l l i s , Lui Carvel lo, and Alex Gartenburg.
To the Scot t fami l y - Chri s, Pam, Chelsea, Megan and A l e x - w h o helped m e t o procras t ina te , provided me w i t h hours of
enjoyment, and most o f a l 1 , f o r t h e i r f r i endsh ip .
1 w i sh t o thank m y mother, Jadwiga. Without her support and
encouragement throughout these fou r years, none o f t h i s would be
possi b l e.
F i n a l l y , a huge thank you t o m y w i f e T ina f o r her patience,
understanding, guidance, and support, from beginning t o end.
Ja kocham c i ebie.
TABLE OF CONTENTS
ABSTRACT
ACKNOWLEDGEMENTS
L I S T OF FIGURES
L I S T OF TABLES
L I S T OF ABBREVIATIONS
CHAPTER
1 INTRODUCTION
2 REVIEW OF THE LITERATURE 2.1 O v e r v i e w 2 . 2 S t u d i e s a n d R e s u l t s 2 . 3 Inferences and C r i t i q u e s 2 .4 Summary
3 A P R I O R I MODEL 3.1 P r i c e - A d j u s t m e n t M e c h a n i sm 3.2 R a t i o n a l e 3.3 L i m i t a t i o n s o f the Mode1 3.4 H y p o t h e s e s
4 METHODOLOGY 4 . 1 A r e a s of S t u d y 4 . 2 S a m p l i n g F r a m e 4 . 3 D a t a C o l l e c t i o n
5 RESULTS AND DISCUSSION 5.1 O v e r v i e w 5.2 H y p o t h e s i s One : P r i c e - A d j u s t m e n t M e c h a n i s m
5 .2 .1 Ind i v i dual R e g r e s s i ons 5 . 2 . 2 P o o l ed D a t a R e g r e s s i on
5.3 T h e N a t u r a l V a c a n c y R a t e 5 . 4 H y p o t h e s i s Two: D e t e r m i n a n t s o f t h e N a t u r a l
V a c a n c y R a t e 5 .5 D i scussi on
6 CONCLUSION 6.1 L i m i t a t i o n s 6 . 2 F u t u r e R e s e a r c h 6.3 Summary
REFERENCES
APPENDIX A: I n d i v i d u a l S t a t i s t i c a l R e p o r t s
APPENDIX B: P o o l e d D a t a S t a t i s t i ca l R e p o r t
APPENDIX C: N a t u r a l V a c a n c y S t a t i s t i c a l R e p o r t
V I T A AUCTORIS
L I S T OF FIGURES
FIGURE 1 : Rental P r i ce-Ad justment Mechani sm
FIGURE 2 : Areas of Study
LIST OF TABLES
TABLE 1 : Ind iv idua l Regressions Summary
TABLE 2 : Pooled Data Regression Summary
TABLE 3: Natura l Vacancy Rate Regression Summary
LIST OF ABBREVIAT f ONS
b l
b
be ta X
BMVR
CA
CMA
CMHC
cPOP
cSH
D
9
MOB
NVR, Vn
OVR, VR
PCR
RNT
R
R*
Rm
Rn
PS
S
S t a t C a n
U . S .
VL
Slope w i t h respect t o vacancies
Regressi on coe f f i c i en t
I n t e r c e p t as a f u n c t i o n o f independent v a r i a b l e s X
Balanced market vacancy r a t e
Census aggl omerati on
Census metropol i t a n area
Canada Mortgage and Housing Corporat ion
Average annual growth i n populat ion
Average annual change i n t o t a l housing s tock
Quanti t y o f r en ta l housi ng demanded
Rate of change
Renter m o b i l i t y r a t e
Natura l vacancy r a t e
Observed o r cu r ren t vacancy r a t e
Percent change i n r e n t
P r i ce o f r en ta l accornmodati on, r en t
Cor re l a t i o n c o e f f i c i e n t
C o e f f i c i e n t o f determi na t i on
Mean l e v e l o f ren ts
Nominal r e n t
Populat ion s i ze
Quanti t y o f r en ta l housi ng suppl i e d
S t a t i s t i CS Canada
Un i ted States o f America
Level o f vacancies (number o f vacant un i t s )
CHAPTER ONE
INTRODUCTION
I n l a r g e urban areas, a two t o t h r e e percent vacancy r a t e i n
t h e p r i v a t e r e n t a l apartment sec to r i s supposed t o i n d i c a t e a
balanced market t h a t "provides r e n t e r s . . . w i t h reasonable
choi ce. . . " [Canada Mortgage and Housi ng Corporat ion, 1996, p. i ] . The vacancy r a t e i s t h e percentage o f u n i t s a v a i l a b l e f o r
immedi ate occupat ion a t a speci f i c p o i n t i n t ime. The balanced
market vacancy r a t e (BMVR) i s used by t r a d i t i o n a l housing
ana lys ts i n comparing two o r more urban areas (Clayton Research,
1994). However, t h i s approach does n o t appear t o cons ider what
i mpact, i f any, v a r i ous soci O-economi c v a r i ab1 es coul d have on
t h e supply o f and demand f o r u n i t s i n t h e p r i v a t e r e n t a l
apartment s e c t o r . An a l t e r n a t i v e approach i s t o use t h e na tu ra l
vacancy r a t e (NVR).
Gabr ie l and N o t h a f t (1988, pp. 420-421 ) d e f i n e t h e n a t u r a l
vacancy r a t e "as t h a t [vacancy] r a t e a t which r e a l r e n t
i ncreases equal zero. " I n t h e i r mode1 , Rosen and Smith (1983,
p.784) assume t h a t d i f ferences between c i t i e s , i n such f a c t o r s as
government regu l a t i o n s , "have had s u f f i c i en t t ime t o have
a f f e c t e d t h e market t t . Th is suggests t h a t t he NVR i s p r e f e r a b l e t o
t h e balanced market vacancy r a t e as a market i n d i c a t o r because i t
consi ders v a r i ous soc i O-economi c and demographi c v a r i ab1 es.
S i nce t h e NVR takes i n t o cons ide ra t i on government
regu la t i ons , among o t h e r f ac to rs , i t may be use fu l i n eva luat ing,
modi f y i ng o r devel op i ng po l i cy. Therefore, t he NVR cou l d be used
i n con junc t ion w i t h t h e observed vacancy ra te , t o determine i f a
c e r t a i n po1 i c y o r group o f po1 i c i e s have s a t i s f i e d any goals o r
c r i t e r i a . I n terms o f urban p lanning, t h e NVR, when a p p l i e d t o
1
s p e c i f i c areas o f a mun i c i pa l i t y o r t o s p e c i f i c dwe l l ing types
could be use fu l i n encouraging and/or p r o t e c t i n g those
developments t h a t he lp t o balance t h e market. This i s impor tant
s i nce i t i s i 1 l e g a l i n Ontar io to recommend o r deny development
app l i ca t i ons on t he bas is o f tenure.
The i n t e n t o f t h i s research i s t o t e s t t he NVR as a market
i n d i c a t o r and t o evaluate t he determinants o f the NVR. The study
complements prev ious work on the NVR by Smith ( 1 9 7 4 ) , Rosen and
S m i t h (19831, Gabr ie l and Nothaf t (1988) and others, and r e l i e s
on t h e empi r i ca l framework l a i d ou t by these authors. Several
l i m i t a t i o n s o f t h e study and suggestions fo r f u t u re research w i l l
a l so be presented.
CHAPTER TM)
REVIEW OF THE LITERATURE
2 . 1 Overview
The focus o f t h i s rev iew o f t h e 1 i t e r a t u r e w i 11 be on t h e
r e l a t i o n s h i p between t h e pr ice-ad justment process and t h e NVU as
d iscussed i n t he i n t r o d u c t i o n . The l i t e r a t u r e review w i l l f o l l o w
t h e e v o l u t i o n o f research methodology as i t app l i es t o t h e above
r e l a t i o n s h i p . Beginning w i t h Smith (l974), t h e review w i 11 then
focus on t h e impor tant work by Rosen and Smith (1983) which forms
t h e b a s i s of t he works t o f o l l o w . Gabr ie l and Notha f t (1988) and
Reece (1988) attempt t o suppor t and extend t h e r e s u l t s o f Rosen
and Smith (1983 ) . An a l t e r n a t i v e use o f t h e NVR i s proposed by
Jud and Frew ( 1990 ) .
2.2 S tud ies and Results
Smith (1974) and Rosen and Smith (1983) bo th hypothesised
t h a t changes i n r e n t s were a f f e c t e d by vacancy ra tes . U t i l i z i n g
annual r e n t a l r e s i d e n t i a l da ta f o r f i v e Canadian c i t i e s f o r t h e
1961 t o 1971 per iod, Smi th ( 1 9 7 4 ) used a m u l t i p l e l i n e a r
regress ion t o test t h e above hypothesis. Rosen and Smith (1983)
used a pool ed c ross -sec t i on ti me-seri es anal y s i s f o r t h e years
1969 t o 1980 f o r 17 American c i t i e s . T h e i r regress ion equat ion i s
sirni l a r t o t h a t p u t f o rward by Smith ( 1 9 7 4 ) . The r e s u l t s o f bo th
s t u d i es i nd i cated t h a t vacanci es a re s i g n i f i can t i n exp l a i n i ng
percentage changes i n r e n t s a t t h e 95 percen t l e v e l (Smith, 1974,
p . 480; Rosen and Smith, 1983, p. 781 ) . Rosen and Smith a l s o
exami ned t h e NVR f o r t h e above 17 c i t i e s .
A NVR was c a l c u l a t e d f o r 14 o f t h e 17 c i t i e s - a reasonable
NVR cou ld n o t be c a l c u l a t e d f o r t h r e e o f t h e c i t i e s because
e i t h e r t h e mode1 d i d n o t h o l d f o r those c i t i e s o r t h e NVR seemed
3
"unreasonably h igh t t (p. 783). Rosen and Smith (1983) a l s o
hypothesised t h a t the NVR i s a f u n c t i o n o f c i t y s i r e , average
ren t , average annual change i n popu la t i on and t o t a l housing
stock, r e n t e r m o b i l i t y ( t he percent o f r e n t e r s moving i n a g i ven
year) , and t h e d i s p e r s i o n o f ren ts . M u l t i p l e l i n e a r regress ion
was used t o es t ima te t h e NVR. The r e s u l t s i n d i c a t e d t h a t t h e NVR
i s higher i n c i t i e s t h a t experience a h ighe r degree o f t u r n o v e r
( t he number o f u n i t s be ing vacated d u r i n g a s p e c i f i c t ime
per iod) . An ex tens ion o f Rosen and Smi th ' s work was c a r r i e d o u t
by Gabr ie l and N o t h a f t (1988).
Gabr ie l and Nothaf t (1988) used rne t ropo l i tan vacancy r a t e
data cornpiled by t he Bureau o f Census r a t h e r than us ing p r i v a t e
rea l es ta te data. Data was c o l l e c t e d f o r 16 U.S. m e t r o p o l i t a n
areas f o r t h e years 1981 t o 1985. Gabr ie l and Nothaf t (1988, p .
421 ) de f ined t h e NVR "as t h a t r a t e a t which r e a l r e n t increases
equal zero. ' ' It was hypothesized t h a t r e a l r e n t s would respond t o
excess supply o f o r demand f o r r e n t a l housing. NVRs were
e s t i mated by u s i ng a pooled c ross-sec t i on t i me-seri es anal y s i S .
Two types o f NVRs were ca lcu la ted : 1) exogenous, where t h e r a t e
i s in f luenced by f a c t o r s externa l t o t h e study area; and 2 )
endogenous, where t h e vacancy r a t e i s i n f l uenced by f a c t o r s
in te rna1 t o the area. The research f i n d i n g s concurred w i t h Smith
(1973) and Rosen and Smith ( l983) , i n t h a t changes i n r e a l r e n t s
were s e n s i t i v e and p o s i t i v e l y c o r r e l a t e d t o dev ia t i ons i n t h e
cur ren t vacancy r a t e from t h e NVR.
Reece (1988) s e t o u t t o expand upon t h e research done by
Rosen and Smith (1983). H i s a i m was t o p r o v i d e a d d i t i o n a l
evidence i n suppor t o f t h e pr ice-ad justment mechanism as p u t
forward by Rosen and Smith (1983, pp. 779-780). To t h i s end,
Reece u t i l i zed annual r e s i d e n t i a l da ta s e r i e s f o r seven U.S.
c i t i e s t h a t predated t h e t i m e p e r i o d used i n Rosen and Smith
(1983). A m u l t i p l e l i n e a r regress ion w a s used t o es t imate t h e
p r i c e s o f r e n t a l u n i t s . The r e s u l t s o f t h e regress ion c o n f i rmed
t h a t t h e c u r r e n t vacancy r a t e was a s i g n i f i c a n t v a r i a b l e i n
exp la in ing changes i n r e n t . (Reece 1988, pp. 412-415)
In a depar tu re from p rev ious research, Jud and Frew (1990)
focused on an a d d i t i o n a l v a r i a b l e . They hypothesised t h a t t h e
more a t y p i c a l ( d i f f e r e n t i n terms o f q u a l i t y and fea tu res ) an
apartment u n i t , t h e h igher t h e NVR f o r t h a t u n i t . Data w a s
c o l l e c t e d f rom two surveys o f apartment p r o j e c t s i n t h e
Greensboro Met ropo l i tan Area were conducted between 1988 and
1989. An annual mai l-survey asked, among o the r t h i n g s , t h e number
of apar tments a v a i l a b l e f o r r e n t and the q u a l i t y o f t h e
apartments (Jud and Puryear, 1989). A mu1 t i p l e 1 i near regress ion
w a s used t o est imate the NVR. The r e s u l t s showed t h a t t h e more
a t y p i c a l an apartment u n i t , t h e h igher t h e NVR and t h e h igher t h e
r e n t assoc ia ted w i t h t h a t u n i t . An examination o f t h e inferences
made by the var ious authors f o l l o w s .
2 .3 In fe rences and C r i t i q u e s
The main in fe rence made i s t h a t changes i n r e a l r e n t a re
p o s i t i v e l y c o r r e l a t e d t o t h e d i f f e r e n c e between t h e c u r r e n t
(observed) vacancy r a t e and t h e NVR (Smith, 1974; Rosen and
Smith, 1983; Gabr ie l and Notha f t , 1988; and Reece, 1988).
A d d i t i o n a l l y , i t i s a lso i n f e r r e d by these authors t h a t t he NVR
i s d i f f e r e n t f o r each m u n i c i p a l i t y . Rosen and Smith (1983, pp.
783-784) suggested t h a t t h i s i s p a r t l y due t o t h e t 'mob i l i t y o r
market t u rnove r o f tenants1' and t h a t d i f ferences between c i t i es,
such as government regu la t ions , "have had s u f f i c i e n t t i m e t o have
a f f e c t e d t h e market1'. However, despi t e concur r ing w i t h Rosen and
Smith (1983) and t h e i r pr ice-ad justment mechanism, Reece (1988)
pondered i f t h e r e was a need t o more p r e c i s e l y e x p l a i n t h e e f f e c t
o f changes i n r e n t on p r i c e expectat ions.
Beyond t h e cornmon theme o f a r e l a t i o n s h i p between r e n t a l
p r i ces , c u r r e n t vacancy r a t e s and t h e NVR, l i t t l e was o f f e r e d i n
terms o f p o s s i b l e exp lanat ions and fu tu re research d i r e c t i o n s .
Rosen and Smith (1983) b r i e f l y mentioned i n a f o o t n o t e t h a t t h e
NVR model d i d no t ho ld f o r two o f t h e c i t i e s and t h a t t h e NVR was
t oo h igh f o r another. There was no attempt t o e x p l a i n these
f a i l u r e s o f t h e model. Th i s leaves t h e model open t o c r i t i c i s m .
Gabr ie l and Nothaft (1988) o n l y went as f a r as suppor t ing
t h e above r e l a t i o n s h i p as proposed by Rosen and Smith (1983).
Reece (1988, pp. 416-417) d i d suggest t h a t p r i c e expectat ions may
p l a y a r o l e i n t h e pr ice-adjustment mechani sm, bu t , t h a t ' 'the
task o f mode l l ing p r i c e expec ta t ions" would be a d i f f i c u l t
exerc i se due t o t h e " l a rge number o f p a r t i c i p a n t s i n t h e housi ng
market. " 3ud and Frew (1990) went beyond t h e p r i c e mechani sm-
vacancy r a t e hypothesi s by exami n i ng t he r e l a t i onshi p between t h e
a t y p i c a l i t y o f r e n t a l u n i t s and t h e l e v e l o f t h e NVU.
Jud and Frew (1990, pp. 300-301) i n f e r r e d t h a t changes i n
r e n t were i n v e r s e l y r e l a t e d t o t h e r a t e o f vacancy i n t h e
previous pe r i od . They also s t a t e d t h a t " the more t y p i c a l an
apartment u n i t , t h e lower t h e n a t u r a l r a t e o f vacancy." However,
there w a s no attempt t o e x p l a i n these r e l a t i o n s h i p s . Despi te
t h i s problem, Jud and F r e w (1990) e x h i b i t e d how t h e NVR may be
used f o r speci f i c market segments and speci f i c dwel 1 i ng types.
2 . 4 Summary
The a p p l i c a t i o n o f NVRs t o t he p r i v a t e r e n t a l apartment
market has several strengths, inc lud ing , the p o s s i b i l i t y t o
est imate speci f i c NVRs f o r s p e c i f i c urban areas, and s p e c i f i c
market segments such as townhouse apartments. Among t he
weaknesses, t h e i mpact o f government regul a t i ons on ren ts i s not
c l ea r i n the 1 i te ra tu re , and t h e lack of representa t ive data f o r
a study area cou ld understate o r overstate the NVR leading t o
f a l s e inferences and/or statements.
Despite these weaknesses, which t h i s paper w i l l not attempt
t o c o r r e c t , t he NVR model, as described by Rosen and Smith
(1983) and Gabr ie l and Nothaf t (1988), w i l l form the
methodological approach t o be used i n the thes i S . Un1 i ke the
ma jo r i t y o f prev ious work, t h e exception being Smith (1974) , t h i s
study wi11 use Canadian data t o t e s t these models.
CHAPf ER THREE
A PRIORI MODEL
3.1 P r i ce-Ad justment Mechani s m
The a p r i o r i model (F igu re 1 ) t o be used i n t h i s t h e s i s i s
based on t h e t h e o r e t i c a l and emp i r i ca l work done by Rosen and
Smith (1983). The work o f o t h e r researchers (Hendershott and
Haurin, 1988; Gabr ie l and No tha f t , 1988; Jud and Frew, 1990) i s
al so i ncorporated i n t o t h i s model where noted. The model used by
Rosen and Smith (1983) i s an example o f a pr ice-adjustment
mechanism. Th i s mechanism a t tempts t o show how a p r i c e of a good
o r se rv i ce i s changed i n response t o changes i n sorne predef ined
s e t o f va r i ab les . Rosen and Smith (1983, p. 779) i d e n t i f i e d t h e i r
mode1 as the " r e n t a l pr ice-adjustment mechani sm. "
The p r i ce-adjustment mechani sm and t h e r e n t a l housi ng market
operate i n a s tock- f tow manner. A t any one t ime t h e r e i s a
q u a n t i t y of r e n t a l housing u n i t s supp l ied and demanded. Rosen and
Smith (1983) suggested t h a t demand, O , i s a f u n c t i o n o f t h e
p r i c e o f r e n t a l accommodation ( r e n t ) , RNT, and o t h e r f a c t o r s , F,
(such as t h e c o s t o f homeownership, r e a l income per household,
and demographic v a r i a b l e s ) , as s e t out i n :
(1 ) D = d(RNT,F) .
The q u a n t i t y o f r e n t a l housing suppl ied, S, i s dependent,
among o ther f a c t o r s , on t h e l e v e l o f r e n t . Ho ld ing these o ther
f a c t o r s constant , t h e h ighe r r e n t , t h e g rea te r t h e i n c e n t i v e t o
i n t roduce new u n i t s t o t h e market. However, i n t h e s h o r t term t h e
q u a n t i t y o f u n i t s supp l ied i s assumed t o be f i x e d . The l e v e l of
vacanci es, VL, i s t h e d i f ference between t h e quant i t y demanded
and suppl i ed :
( 2 ) VL=S-O.
FIGURE 1 RENTAL PRICE-ADJUSTMENT MECHANISM
Units Vacant
471 Tl Real Incorne - 7 ' Demographic
Observed Vacancy Rate
(OVR) V
Owning
Units Demanded
Natural Vacancy Rate
(NVR) Vn
Per Household
Rental Price Adjustment Mechanism
OVR < NVR (; ~ o v R > f U v R
Variables
I I 1 Source: Author. 1998.
9
Rent Conf rot
No Change 1
+ lncrease OVR = NVR Decrease Rent Rent
The observed o r cu r ren t vacancy r a t e (OVR), V, i s t h e number o f
vacant u n i ts , VL, d i v i ded by t h e q u a n t i t y of r e n t a l u n i t s
suppl ied, S:
( 3 ) V = VL/S = 1-(1/S) * d(RNTjF)
The r e n t a l p r i c e adjustment mechanism i s o u t l i n e d i n
equat ion 4 and i s based on Gabr ie l and N o t h a f t ' s (1988, p. 421)
modi f i c a t i on o f Rosen and Smith ' s (1 983) research. Devi a t i ons i n
the OVR f rom t h e NVRj Vn , determines changes i n t h e r e a l r e n t o f
rental u n i t s . The r a t e o f change, g, i n nomi na1 ren ts , R n , i s a
f u n c t i o n o f t h e d i f f e rence between the NVR and t h e OVR:
( 4 ) Rn = g(Vn-V).
It i s assumed t h a t over t h e es t ima t i on p e r i o d t h e NVR i s
constant b u t v a r i e s according t o market c o n d i t i o n s and t h a t Vn
may be i ncorporated i n t o t h e i n t e r c e p t . T h e e s t i m a t i n g model i s
w r i t t e n as:
( 5 ) PCR = (be ta X ) + b iV
where PCR i s t h e percent change i n ren t , b e t a X i s t h e i n t e r c e p t
and b i i s t h e s lope. I n a d d i t i o n t o t h e above s p e c i f i c a t i o n ,
Rosen and Smith (1983) among others , pooled da ta cross-sect ion
t i m e - s e r i es regress ion were est imated w i t h c i t y dummy v a r i ables.
This poo led da ta model i s w r i t t e n as:
S - l
(6) PCR = (be ta X ) + C b j c j + b i V j = I
where N i s t h e number o f c i t i e s pooled i n t h e ana l ys i s , bj a r e
regress ion c o e f f i c i e n t s and c j are t h e area dummy va r i ab les . NVR
i s t h e r a t e a t which changes i n real r e n t s a r e equal t o zero.
From equat ion 6 t h e NVR can be i n t e r p r e t e d as f o l l o w s :
( 7 ) V n i = (be ta X + b i ) / b i .
I n t h e s h o r t r u n i t i s assumed t h a t t h e NVR i s cons tan t .
Di f ferences between t h e OVR and t h e NVR r e f l e c t e i t h e r an excess
supply o f r e n t a l housi ng (NVR i s l e s s than OVR) o r an excess
demand f o r r e n t a l housing (NVR i s g rea te r than OVR). I n t h e
former case, r e n t s should inctease, whereas i n t h e l a t t e r case,
r e n t s should decrease. I f the NVR equals t h e OVR, r e n t s should
no t change. A f o u r t h op t i on i s found i n t h e form o f r e n t c o n t r o l .
R e n t c o n t r o l breaks t h e 1 i nkages i n t h e pr ice-adjustment
mechani sm. Typi c a l r e n t c o n t r o l l e g i s l a t i o n 1 i m i t s t h e number o f
t imes r e n t can be increased i n a g iven p e r i o d and/or t h e amount
o f t h a t increase (Smith and Tomlison, 1981, pp. 94-97). Thus, i t
i s no t always p o s s i b l e t o increase r e n t t o the l e v e l suggested by
t h e pr ice-adjustment mechanism. These changes, i f any, i n ren ts ,
loop back t o t h e s t a r t o f t he mechanism and a f f e c t t h e q u a n t i t y
of u n i t s supp l i ed and demanded.
3 .2 Rat iona le
The r a t i o n a l e f o r us i ng t h e proposed model i n F i g u r e 1 and
t h e accompanying equations i s based l a r g e l y on t h e acceptance of
the Rosen and Smith (1983) model i n t h e 1 i t e r a t u r e . L i t e r a l 1y
every author i n t h i s f i e l d has used t h e Rosen and Smith model as
t h e bas is o f t h e i r research (Gabr ie l and Nothaf t , 1988;
Hendershott and Haur in, 1988; Read, 1988; Reece, 1988; V o i t h and
Crone, 1988; Wheaton and Torto, 1988; 3ud and Frew, 1990). These
authors d i d no t employ the model exp l i c i t l y - changes were made
t o r e f l e c t t h e na tu re o f t h e i r s p e c i f i c research o b j e c t i v e s . Th is
demonstrates t h e v e r s a t i l i t y o f t h e model f o r o the r research
purposes. For example, Vo i th and Crone (1988), Wheaton and Tor to
(1988) and Benjamin e t a l (1997) used t h e Rosen and Smith model
t o est imate NVRs f o r t h e commercial o f f i c e sec to r . I n a d d i t i o n ,
no l i t e r a t u r e has been found t h a t r e f u t e s o r chal lenges t h e work
performed by Rosen and Smith. The l a c k o f any chal lenge t o t h i s
model i s a s t r o n g i n d i c a t o r t h a t t h e mode1 i s a sound one. That
sa id , t he re i s a need t o t e s t t he model us ing cu r ren t da ta f o r
Canadian urban areas.
3.3 L i m i t a t i o n s o f the model
Despi t e t h e smooth fl ow o f t he model dep ic ted i n F igu re 1,
t h e r e n t a l housing market i s sub ject t o t i m e lags and government
regu la t i on . Tenants t y p i c a l l y s ign leases o f 12 months. I n t h e
Province o f Ontar io , p r i o r to June 1998, l e g i s l a t i o n requ i red
t h a t a tenant p r o v i d e two months n o t i c e p r i o r t o vaca t ing t h e
u n i t , pe rm i t t ed one increase i n r e n t p a i d per 12 month per iod ,
and l i m i t e d t h e amount o f t h a t increase. A s of June 1998, t h e
Province of On ta r i o brought i n a systern o f vacancy decont ro l .
Under t h i s system, when a u n i t becomes vacant, t he re i s no cap on
t h e rent t h a t may be charged. Once t h e u n i t i s occupied r e n t
cont r o l appl i es and r e n t i ncreases a r e regu l ated. The Prov i nce
expects t h a t t h i s new system o f r e n t c o n t r o l w i l l encourage t h e
cons t ruc t i on o f r e n t a l u n i t s i n Ontar io . Th i s may reduce t h e tirne
l ags and t h e degree o f government i n t e r v e n t i o n i n t h e r e n t a l
housi ng market.
3.4 Hypotheses
Based on t h e o b j e c t i v e s o f t h i s s tudy, t h e review o f t h e
l i t e r a t u r e and t he a p r i o r i model, t h e f o l l o w i n g hypotheses w i l l
be eval uated :
1. A f u n c t i o n a l r e l a t i o n s h i p e x i s t s between the percent change i n r e n t and t h e observed vacancy r a t e .
2 . The n a t u r a l vacancy r a t e i s a f u n c t i o n o f : i ) t h e mean l e v e l of ren ts ; i i ) popu la t i on size; i i i) r e n t e r mobi 1 i t y ra te ; i v ) t h e average annual change i n t o t a l housing stock; and v ) the average annual growth i n popu la t i on .
The f i r s t hypothesi s i s based on the p r i ce-adj ustment mechani s m
mode1 forwarded by Rosen and Smith (1983) . The second hypothesis
i s based on Rosen and Smith (1983) and Gabriel and Nothaft (1988)
who in fe r red that the population o f a c i t y and the m o b i l i t y of
t h a t population have an impact on the l e v e l o f the NVR.
CHAPTER FOUR
METHODOLOGY
4 . 1 Areas o f study
The areas o f s tudy i n F igure 2 cons i s t o f s i x ce nsus
m e t ropol i tan areas (CMA) : Hami 1 ton, K i tchener, London, Sudbury,
S t . Catharines-Niagara and Windsor, and four census
aggl omerati ons (CA) : B a r r i e, Guelph, K i ngston and Peterborough,
a l 1 i n t he Province of Ontar io. The s i t e s a re in f luenced by
surroundi ng areas i n two ways: 1 ) employrnent opportuni t i e s ; and
2) housing cos ts . The in f luence o f surrounding areas i s m i n imized
by using t he CMA as t h e bas is f o r d e f i n i n g a study area.
The use o f t h e CMA/CA as a means o f d e f i n i n g t h e study area
i s j u s t i f i e d as f o l lows. The S t a t i s t i c s Canada (StatCan)
d e f i n i t i o n o f a CMA/CA i s based on place-of-work data. Data i s
r e a d i l y a v a i l a b l e a t t h e CMA/CA l e v e l . CMHC, StatCan and var ious
housi ng anal ysts ( C l ayton, 1994) u t i 1 i ze t h e CMA/CA when
comparing housing market i nd i ca to r s between urban areas. The
var ious works discussed i n Chapter Two used the CMA/CA, o r
equi valent, i n t h e i r ana lys i s o f the NVR (Smith, 1974; Rosen and
Smith, 1983; Gabr ie l and Nothaf t , 1988; and Reece, 1988).
4 . 2 Sampling frame
The sampling frame f o r t h i s mode1 i s based on t h e da ta
gathered by t he CMHC and StatCan, and mod i f i ed as requ i red . The
CMHC c o l l e c t s and disseminates r e n t a l market data f o r every
CMA/CA i n Canada. Th is i s not a complete populat ion. The survey
on l y i n c l udes those r e n t a l u n i t s i n b u i l d i n g s w i t h s i x o r more
u n i t s , as de f ined by CMHC, and was, u n t i l recent ly , c o l i e c t e d
semi-annually. The s tudy pe r iod i s f rom 1988 t o 1996.
Windsor CMA
LEGEND
Study Areo - CMNCA As defined by Statistics Canada
Source: Statistics Canada. 1 992. O 0 O 120 *M.
O 4 O 80 MI.
Cur ren t l y , data i s c o l l e c t e d d u r i n g t h e month o f October each
year and t h e survey now inc ludes r e n t a l u n i t s i n b u i l d i n g s w i t h
t h r e e o r more u n i t s .
A d d i t i o n a l l y , CMA/CA boundaries change over t ime, there fo re ,
i t i s necessary t o know when boundary changes occurred, what
e f f e c t t h i s had on t h e sample s i r e , and what adjustments, i f any,
t o p rev ious data had been c a r r i e d out . StatCan da ta i s a lso
sub jec t t o s imi l a r problems i n terms o f changes i n CMA/CA
boundaries. The r a t i o n a l e f o r u t i 1 i z i n g t h i s sampl i n g frarne i s
based on the fac t t h a t t h e above agencies have i n p lace a system
o f sampling procedures and des ign t h a t i s c o n s t a n t l y be ing
moni tored f o r e r ro rs , changes and improvements.
4.3 Data collection
The fi r s t hypothesi s, t h a t t h e percent change i n r e n t i s a
f unc t i on o f t he d i f f e r e n c e between the NVR and OVR, was
eval uated by us i ng c l ass i c a l regress ion anal ys i s on i nd i v i dual
areas and a m u l t i p l e regress ion ana l ys i s on t h e pooled da ta .
Four assumpti ons associ a t e d w i t h c l assi c a l r eg ress i on anal y s i s
a re as f o l l o w s : 1) t h e reg ress ion ana lys is f i t s a s t r a i g h t l i n e
through t h e s c a t t e r p l o t o f da ta po in ts ; 2 ) f o r every va lue o f t h e
i ndependent va r i ab le ( x ) , t h e d i s t r i b u t i o n o f r e s i d u a l values (Y-
Y i ) should be normal l y d i s t r i b u t e d w i t h zero means; 3 ) t ha t no
a u t o c o r r e l a t i o n ex i s ts ; and 4 ) homoscedastici t y i s present .
The vacancy r a t e and r e n t da ta was obta ined from CMHC
p u b l i c a t i o n s . The percent change i n r e n t was based on an average
o f t h e mean ren ts p a i d f o r a one-bedroom and two-bedroom p r i v a t e
r e n t a l apartment u n i t i n b u i l d i n g s w i t h s i x o r more u n i t s . 00th
v a r i a b l e s a re f o r t h e month o f October.
I n a d d i t i o n t o t h e above data, t h e pooled da ta ana lys is used
ni ne dummy v a r i ab1 es. Each dummy v a r i ab1 e represented a speci f i c
area of study. The t e n t h area, Windsor, was i n d i c a t e d by a l 1 n ine
dummy v a r i ab1 es hav i ng a v a l ue o f zero.
The second hypothes is , t h a t t h e NVR i s a f u n c t i o n o f var ious
v a r i ables, u t i 1 i zed a m u l t i p l e regress ion a n a l y s i S . A t - t e s t was
used t o t e s t v a r i a b l e s f o r s ign i f i cance (Rosen and Smith, 1983;
Hendershott and Haur in, 1988, p . 346; Gabr ie l and Nothaf t , 1988).
Based on t h e work by Rosen and Smith (1983), t h e ad justed mode1
t o est imate t h e determinants o f t h e NVR i s :
( 8 ) Vn = ~ (Rrn j PS, MOB, cSH, cPOP)
where Rm i s t h e mean l e v e l of ren ts , PS i s t h e popu la t ion s ize ,
MO6 i s t he r e n t e r m o b i l i t y r a t e , cSH i s t h e average annual
change i n t o t a l housing stock, and cPOP i s t h e average annual
growth i n popu la t ion . Given a confidence l e v e l o f 95 percent and
degrees o f freedorn equal t o n-2 where n i s t h e sample s ize, i f t
observed exceeds t - c r i t, w e r e j e c t t h e nu l 1 hypothesi s and i nfer
t h a t t h e i ndependent v a r i a b l e i s s i g n i f i c a n t .
The NVR was c a l c u l a t e d us ing equat ion ( 7 ) and data from t h e
fi r s t hypothesis. The mean l e v e l of r e n t s was t h e average r e n t
over t h e study p e r i o d . Populat ion s i z e and average annual growth
i n popu la t ion data were obta ined from t h e Census o f Canada. The
r e n t e r m o b i l i t y r a t e was based on rnover data. T o t a l occupied
household data was used as a proxy f o r t o t a l housing stock. I n
t h e Census, one household equals one occupied dwe l l i ng . Th is
method w i l l underest imate t h e t o t a l housing s tock, however, i n
t h e 1991 Census, p r i va te unoccupi ed dwel 1 i ngs accounted f o r no
more than 6.4 percent o f t o t a l p r i v a t e d w e l l i n g s o f t he s i x CMAs
i n t h i s study ( S t a t i s t i c s Canada, 1992).
CHAPTER F I V E
RESULTS AND DISCUSSION
5.1 Overview
The chapte r w i 11 f i r s t p resen t t h e r e s u l t s o f t h e f i r s t
hypothesi s i n regards t o t h e r e n t a l pr ice-adjustment mechani sm
i n c l udinç i n d i v i d u a l regress ions f o r each study area and t h e
pooled data reg ress ion . Th is i s f o l lowed by t h e c a l c u l a t i o n o f
t h e NVR f o r each area o f s tudy. The t h i r d sec t i on w i l l present
t h e r e s u l t s o f t h e second hypothes is i n regards t o t h e
determinants o f t he NVR. Concluding thoughts about t h e r e s u l t s
a r e o f f e r e d i n t h e f i n a l s e c t i o n .
5 .2 Hypothesi s One - Rental P r i ce-Adjustment Mechani sm
A summary i s prov ided i n Tables 1 and 2 on t h e f o l lowing
page f o r t h e i n d i v i d u a l study area regress ions and t h e pooled
data regress ion, r e s p e c t i v e l y . Deta i l e d s t a t i s t i c a l r e p o r t s a re
prov ided i n Appendices A and B.
5 .2 .1 I n d i v i d u a l Regressions
The t e s t assumptions o f t h e regress ion analyses were
s a t i s f i ed. However, i n t he case o f t h e i nd iv idua l regressions,
t h e Durbi n-Watson s t a t i s t i c i nd ica tes t h a t some p o s i t i v e
a u t o c o r r e l a t i on does ex i s t f o r B a r r i e , Hami 1 ton, K i tchener and
Sudbury. There i s a poss ib i 1 i t y o f temporal c o r r e l a t i o n , s ince a
r e n t a l u n i t may be vacated and leased several t imes d u r i ng the
study pe r i od . S ince the sample s i z e i s small i n the i n d i v i d u a l
regress ions - o n l y e i g h t observa t ions - i t i s d i f f i c u l t t o i n f e r
t o what degree a u t o c o r r e l a t i o n e x i s t s , i f a t a l l . Due t o t ime
cons t ra i n t s , no c o r r e c t i o n f o r a u t o c o r r e l a t i o n was made i n t h i s
study. i t should be noted t h a t p r e c i s i o n o f t h e r e s u l t s m a y be
reduced.
18
TABLE 1 INDIVIDUAL REGRESSIONS SUMMARY
HYPOTHESIS ONE
Barrie CA .- - - -
Guelph CA
Hamilton CMA - - -
Kingston CA
Kitchener CMA -
London CMA
Peterborough CA
St-Catharines-Niagara CMA - " -
Sudbury CMA
Windsor CMA
Dependent Variable: lndependent Variable: Source: Author, 1998.
Percent Change in Rent Vacancy Rate
TABLE 2 POOLED DATA REGRESSION SUMMARY
HYPOTHESIS ONE
I F ( ~ O. 69) = 3.643 p < 0.001; Durbin-Watson = 1.71 3
Dependent Variable: Percent Change in Rent N=80 Independent variables: 10; Vacancy Rate plus nine Dummy Variables Source: Author, 1998.
NVR
- -
4.14
4.92
4.75
4.97
5.97
6.43
6.22
6.81
6.28
5.30
Variable
Constant
Vacancy Rate - .-
Barrie CA
Guelph CA
Hamilton CMA - - .
Kingston CA
Kitchener CMA . .
London CMA -
Peterborough CA
St-Catharines-Niagara CMA
Sudbury CMA
Windsor CMA
R = 0.588; R' = 0.346; Adjusted - R'= - - 0.251 - - .
b t (69) Sig.
0.0654 6.966 100.0% - -
-0.01 23 -5.895 100.0%
-0.0143 -1 -220 77.3% - -
-0.0047 -0.399 30.9% - -
-0.0068 -0.582 43.8%
-0.0004 -0.035 2.8%
0.0083 0.707 51.8%
- 0.01 39 1.148 74.5%
0.01 14 0.953 65.6% - --
0.01 85 1.533 87.0%
0.01 21 1 .O36 69.6%
- - -
The r e s u l t s o f our est imat ion i n d i c a t e t h a t the vacancy r a t e
was s i gni f i c a n t i n exp la i n i ng the percentage change i n r en t s a t
the 90 percent l e v e l i n s i x o f t h e t en areas: iiami l t o n , Kingston,
London, Peterborough, S t . Cathari nes and Sudbury. The resu l t s f o r
Guelph and Windsor were s i gni f i can t a t t he 75 percent 1 evel ,
based on seven degrees o f freedom and a t - c r i t i c a l value o f
0 . 7 1 1 . Therefore, a t t he 75 percent l e v e l e i ght out o f t e n areas
had s i g n i f i c a n t resul t s . The r e s u l t s fo r Ba r r i e and Ki tchener
were i n s i gni f i cant . The regress i on c o e f f i c i e n t s f o r a l 1 ten areas had negat i ve
s igns. T h i s i nd ica tes t h a t an increase i n t he current vacancy
r a t e above the na tu ra l vacancy r a t e should r e s u l t i n a srnaller o r
negative percent change i n ren t . This i s consis tent w i t h supply
and demand theory, i n general, and w i t h Rosen and Smith (1983)
and Gabriel and Nothaf t (1988). I n Hamilton, London, S t .
Cathari nes and W i ndsor the regressi on coef f i c i ents i ndi ca te t h a t
vacancies are more sens i t i ve t o changes i n ren ts than t h e
remai n i ng areas. A sens i t i ve r e l a t i onshi p suggests t h a t
landlords are more responsive t o the vacancy ra te . For example,
if the vacancy r a t e increases, land lords i n these areas are more
1 i kel y n o t t o i ncrease the ren t . The es t imat ion produced the best r e s u l t s fo r S t . Catharines
w i t h a c o r r e l a t i o n c o e f f i c i e n t , R , of 0.957, i n d i c a t i n g a s t rong
and d i r ec t r e l a t i onshi p between the i ndependent and dependent
var iab les, and a c o e f f i c i e n t o f determinat ion, R 2 , o f 0.916. The
independent var iab le , vacancy ra te , exp la ins almost 92 percent of
the t o t a l v a r i a t i o n i n the dependent v a r i able, percent change i n
r e n t . The R and R2 values should be t r ea ted w i t h caut ion. A h igh
R* va l ue may i ndi ca te co l 1 i near i t y between the i ndependent and
dependent v a r i ab1 es. Other areas w i t h s t r o n g r e s u l t s i n c l ude
K i ngston, London and Sudbury. Hami 1 t o n and Peterborough had
moderate c o r r e l a t i o n s c o e f f i c i e n t s , whi l e B a r r i e , Guelph,
K i tchener and Windsor had weak values.
5.2.2 Pooled Data Regression
Table 2 r e p o r t s t h e regress ion r e s u l t s f o r equation ( 6 ) . The
mode1 exp la ins approx imate ly 35 percent o f t h e v a r i a t i o n i n t h e
percent change i n r e n t s . The c o r r e l a t i on c o e f f i c i ent, R,
i nd i cates a moderate d i r e c t r e l a t i onshi p between the vacancy r a t e
and percent change i n r e n t . The F-value o f 3 .64 i s s i g n i f i c a n t a t
t h e 0.01 l e v e l . A u t o c o r r e l a t i o n i s ve ry weak as i nd i ca ted by a
Durbin-Watson va lue o f 1.713. The c o r r e l a t i o n ma t r i x i n Appendix
8 shows very l i t t l e c o r r e l a t i o n between t h e observed vacancy r a t e
and t h e dummy v a r i a b l e s and between t h e percent change i n r e n t
and t h e dummy v a r i a b l e s .
Resul t s i n Tab le 2 i nd ica te t h a t t h e VR c o e f f i c i e n t has a
negat i ve s ign . That i s , a decrease i n t h e vacancy r a t e i s
associated w i t h an increase i n t h e pe rcen t change i n r e n t .
T h i s i s cons i s ten t w i t h t h e f i n d i n g s f rom t h e i n d i v i d u a l
regress ions above and w i t h Rosen and Smith (1983) Gabr ie l and
Nothaf t (1988) and Benjamin e t a l (1997).
5 .3 The Natura l Vacancy R a t e
The NVR was c a l c u l a t e d us ing equat ion ( 7 ) and regress ion
c o e f f i c i e n t s from t h e pooled data reg ress ion . The r e s u l t s a re
presented i n Table 2. The mean n a t u r a l vacancy r a t e i s 5.58
percent . There i s 1 i t t l e v a r i a t i o n i n t h e study area NVRs. Rates
range from a low o f 4.14 percent i n B a r r i e t o a h igh of 6.81 i n
St .Cathar ines, a d i f f e r e n c e o f 2 .67 percentage po in t s . T h i s i s
i n c o n t r a s t t o Rosen and Smith (1983, p. 783), Gabr ie l and
Nothaft (1988, p.425) and Benjamin e t a l (1997, p . Q ) , who found
considerable v a r i a t i o n among met ropo l i tan na tu ra l vacancy rates.
These values are higher than t he two t o t h ree percent
balanced market vacancy r a t e used by CMHC (1996, p . i ) , because i t
i s argued by Rosen and Smith (1983, p.784) t h a t d i f fe rences
between c i t i e s , such as munic ipal government regu la t ions ,
employment opportuni t i es, and t he devel opment envi ronment, have
had ample t ime t o a f f e c t the market. It should be noted t h a t t he
mean na tu ra l vacancy r a t e i s o n l y 2.5 percentage p o i n t s higher
than the balanced market vacancy r a t e used by CMHC and housing
analysts. Th is seems t o suggest t h a t t he BMVR cou ld be increased
t o between f i v e o r s i x percent f o r simple compari son purposes.
Before such a change i s made o r suggested, i t would be wise t o
evaluate t he est imat ing model i n equations (5 ) and (6 ) on
addi t i onal CMA/CAs throughout Canada.
5 .4 Hypothesis Two - Oeterminants o f t he Natural Vacancy Rate
The second hypothesis s t a ted t h a t t he NVR i s a funct ion o f :
i ) the mean l e v e l of rents; i i ) populat ion s i ze; i i i ) ren te r
mob i l i t y ra te ; i v ) the average annual change i n t o t a l housing
stock; and v ) t h e average annual growth i n populat ion. The
est imat ing model i s set out i n equation ( 8 ) . Resul ts o f the
analysis are summarized i n Table 3. A de ta i l e d s t a t i s t i c a l repor t
w i t h addi t i o n a l regression resu l t s and desc r i p t i ve s t a t i s t i c s i s
provided i n Appendix C. Caution should be used i n i n t e r p r e t i n g
these r e s u l t s . The regression ana lys is i s weak due t o a small
sample s i t e - ten observations f o r each o f t he f i v e regressors.
The regress i on has an R o f 0.81, i n d i c a t i ng a s t rong and
d i r e c t r e l a t i onshi p between the na tu ra l vacancy r a t e and t h e
independent var iab les . Almost 66 percent of t he v a r i a t i o n i n t h e
na tura l vacancy r a t e can be explained by t he independent
va r iab les . The Durbin-Watson value i s low a t 1.04, i n d i c a t i n g
there i s some p o s i t i v e au toco r re la t i on . Addi t i o n a l ly, t h e
c o r r e l a t i on ma t r i x i n Appendix C, revea l s some c o r r e l a t i o n
between several independent var iab les . The reader should be aware
t h a t t h i s may a f f e c t t he p rec i s ion o f t h e r e s u l t s . Due t o t ime
cons t ra in t s no co r rec t i on f o r au toco r re la t i on was perfotmed.
The s igns on mean rents , Rm, popu la t ion s i r e , PS, average
annual growth i n populat ion, cPOP, and t he m o b i l i t y r a t e , MOB,
are negative, suggesting t h a t h igher ren ts , l a rge r populat ions,
h igher popu la t ion growth and higher m o b i l i t y ra tes are assoc ia ted
w i t h lower NVRs. Average annual change i n housing stock has a
p o s i t i v e s i gn i n d i c a t i ng t h a t smal l e r changes i n housi ng s tock
are r e l a t e d w i t h lower NVRs. However, none o f t he va r i ab l es a r e
s i gn i f i c a n t a t the 95 percent l e v e l o r h igher .
TABLE 3 HYPOTHESIS TWO REGRESSION SUMMARY
R = 0.809: R'= 0.655; Adjusted R' = 0.225 - - - - - . - - - - - - - - -
F(5,4) = 1.522 p < 0.353; Durbin-Watson = 1 .O40
Dependent Variable: Natural Vacancy Rate Independent Variables: 5 - see above Source: Author. 1998.
5.5 D i scussi on
There seems t o be a func t iona l r e l a t i o n s h i p between t h e
percent change i n r e n t and the observed vacancy ra te . The pooled
data regression expla ined 35 percent o f t h e v a r i a t i o n i n t h e
percent change o f r e n t . The vacancy r a t e was found t o be
s i gni f i can t a t a p - leve l o f 0.05. However, none o f the dummy
v a r i ab1 e regress i on coef f i c i ents were s i gn i f i cant a t t he 95
percent 1 evel . The funct ion between the components ( o r determinants) of the
na tu ra l vacancy r a t e and t he l e v e l o f the na tu ra l vacancy r a t e i s
uncer ta in . Though t h e R value ind ica ted a s t rong and d i r e c t
r e l a t i onsh ip , t he re w a s a vast d i f f e r e n c e between the values of
R2 and the adjusted R * . Add i t i ona l l y , none o f the var iab les were
s i g n i f i c a n t a t t h e 95 percent l e v e l and t he re appeared t o be some
posi t i ve au toco r re l a t i on. Caution should be used i n i n t e r p r e t i n g
these values s ince t h e sample s i ze (N=10) was smal l .
A t f i r s t glance, i t appears t h a t t h e l a r g e r the popu la t ion ,
t h e stronger and more sensi t i ve t he r e l a t i onshi p between percent
change i n r en t and t h e vacancy r a t e i s . Th is holds t r u e fo r four
o f t he f i v e l a r g e s t areas i n the sample. However, there i s a weak
and i ns ign i f i c a n t r e l a t i onshi p f o r Ki tchener, t he t h i r d l a r g e s t
area i n terms o f popu la t ion i n the study.
A p l aus i b l e exp lanat ion may be the extens ive CO-operat ive
(CO-op) education programs o f fe red by t he Un i ve r s i t y o f Waterloo
( U W ) . O f the almost 24,000 students enro led a t U W , about 9,000 o r
38 percent are CO-op students (Un i ve r s i t y o f Waterloo, 1998). It
i s t y p i c a l f o r a CO-op student t o a l t e r n a t e between a term of
school and a term o f work, usual ly f o r f i v e years. S i nce i t i s
cornmon f o r the work term t o be done ou t s i de t h e Kitchener CMA,
some s tudents w i l l seek a four month lease i n s t e a d o f t he common
12-month lease.
To t h e l and lo rd , t h e i m p l i c a t i o n s o f CO-operat ive education
a r e a d d i t i o n a l costs i n regards t o a d v e r t i s i n g t h e u n i t , c r e d i t
and background checks, and r e p a i r i ng/c l eani ng t h e un i t every f o u r
months. To t h a t end, t h e l a n d l o r d may charge a lower r e n t
( e i t h e r a reduc t i on i n t h e a c t u a l r e n t p a i d o r a one-time rebate)
f o r longer- term leases i n o r d e r t o reduce t h e c o s t s mentioned
above, even if t h e pr ice-ad justment mechanism i n equations (6)
and ( 7 ) suggests a r e n t inc rease . That i s , t h e l and lo rd , i n
s e t t i ng t h e r e n t , i s more s e n s i t i v e t o reduc i ng costs than t o t h e
vacancy r a t e .
The poor r e s u l t s o f t h e i n d i v i d u a l and pooled data fo r
several o f t h e study areas may be due t o va r i ous fac to rs .
B e s i des t h e exp l anat ion d i scussed above f o r t h e K i tchener CMA,
another p o s s i b i 1 i t y i s t h e s i z e o f t h e p r i v a t e r e n t a l apartment
market. The d i v e r s i t y and qua1 i t y o f r e n t a l uni t s may be l i m i t e d
i n areas w i t h smal ler popu la t i ons . It i s p o s s i b l e t h a t a tenant
m a y f i nd a u n i t t h a t s u i t s t h e i r needs (number o f rooms,
l o c a t i o n , s i z e , and so on) . Since the market i s n o t as d iverse,
t he tenant m a y have t o pay a premi um f o r t h a t u n i t . Landlords,
b e i ng aware t h a t choice i s 1 i m i ted, are n o t necessar i l y
responsive t o changes i n t h e vacancy r a t e . T h i s p a r t l y exp la ins
t h e low regress ion c o e f f i c i e n t values f o r t h e areas w i t h t h e
smal les t popu la t ion .
CHAPTER S I X
CONCLUSION
obvious weaknesses w i t h t h i s r
6.1 L i m i t a t i o n s
One o f t h e mo esearch was
i t s r e l i a n c e on a very small sample s i r e due t o t ime, budget and
external cons t ra in ts . CMHC on l y began prov id ing ren ta l apartment
market data i n 1988. I n i t i a l l y , data was co l l ec ted
semi-annual l y each year f o r u n i t s i n bu i l d ings w i t h s i x o r more
u n i t s . In 1991, the survey was expanded t o inc lude bu i l d ings w i th
three o r more u n i t s . However, t h i s data i s no t necessar i ly
avai l a b l e f o r a l 1 CAS. S t a r t i n g i n 1996, data w a s co l l ec ted
annual 1 y each October . The mode1 shoul d have i n c l uded addi t i onal
var iab les f o r u n i t s i n bu i l d i ngs w i t h three o r more u n i t s .
Another problem was a change i n the boundary of a CMA/CA. A
few o f t h e study areas had one o r more CMA/CA boundary changes
over the study per iod. S t a t i s t i c s Canada r e a d i l y repor ts the
t o t a l popu la t ion f i g u r e f o r bo th the current and previous census
i n the Census o f Canada pub l i ca t ions . I f t he boundary was changed
f o r the cu r ren t c e n s u s , the previous census t o t a l populat ion i s
adjusted t o r e f l e c t the new boundary. However, adjusted data i s
not readi 1 y avai 1 ab1 e f o r p r i o r censuses - t h i s a l so appl i es t o
mover and dwe l l i ng data. Prec is ion of the r e s u l t s may be reduced
through overstated populat ion and dwel l ing growth ra tes .
One aspect not discussed i n t h i s study i s t h e impact o f ren t
cont ro l on changes i n r en t and vacancy ra tes . The general
e f f ec t s o f r e n t cont ro l are well-known and un ive rsa l . There i s a
vast amount o f l i t e r a t u r e on t h e t op i c . I n t h e beginning, ren t
cont ro l was t o be a temporary measure i n response t o sharp
increases i n r e n t . Over time, t he nature o f r e n t con t ro l ( from a
26
p o l i t i c a l p o i n t o f view) has focused on p ro tec t i ng low-income
households from "unreasonable r e n t increases" and as a
" s u b s t i t u t e f o r p u b l i c housing" (Ho 1992, pp. 1183-1184).
During t h e study per iod, a l 1 r e n t a l u n i t s were sub jec t t o
bo th r en t c o n t r o l and landlord- tenant l e g i s l a t i o n . Annual r en t
increases are capped a t a percentage l e v e l as determined by the
government. I n terms o f t h e model, t h i s has t he e f f e c t o f capping
increases i n r e n t as determined by t h e pr ice-adjustment
mechani sm. Rent con t r o l i n e f f e c t "breaks" t h i s mechani sm. Thus,
i t i s poss ib le t h a t the NVR i s understated f o r a l 1 areas. This
may exp la in w h y t he re i s l i t t l e v a r i a t i o n i n the NVR between
areas and why t h e NVR ra tes appear t o be low compared t o t h e
fi nd i ngs o f o the r authors.
6 .2 Future Research
Several d i r e c t i o n s o f f u t u r e research i n to t h e na tu ra l
vacancy r a t e a r e poss ib le . One p o s s i b i l i t y i s t o expand t h e area
o f study t o i n c l ude a l 1 CMA/CAs i n Canada, data permi tti ng.
Besides inc reas ing t h e sample s i z e , t h i s would pe rm i t researchers
t o compare and analyse the e f f e c t of d i f f e r e n t munic ipa l and
p rov i nc i a l regu l a t i ons on t h e r e n t a l apartment market.
An extension o f the above d i r e c t i o n i s the accommodation o f
r e n t con t r o l i n t he NVR model . Denton e t a l ( l 9 9 4 ) , i n a broad
study i n t o t he e f f e c t o f r e n t c o n t r o l on t he r e n t a l housing
market, hypothesized t h a t V e n t regu la t i ons are assoc ia ted w i t h
1 o w e r vacancy ra tes , other t h i ngs [be i ng] equal . " (p. 3 ) . Though
"scept ica l t l o f t h e r e s u l t s from t e s t i ng t h i s hypothesi s, Denton
e t a l p rov i ded a t heo re t i c a l and methodologi ca l s t a r t i ng po i n t
f o r t h e i n c l u s i o n o f ren t c o n t r o l i n NVR model.
More a t t e n t i o n could be focused on the du ra t i on o f the
vacancy o f a u n i t ra the r than t h e vacancy ra te . Th is would
introduce a more dynamic approach ta market behaviour. Read
( 1 988) attempted t o formulate a mode1 t h a t expl a i ns vacancy
durat ions i n r e l a t i o n t o adve r t i s i ng and the NVR. However, h i s
paper only presented the t heo re t i ca l framework t o permi t
empi r i ca l t e s t i ng . 6.3 Summary
The i n t e n t o f t h i s research was t o t e s t the NVR as a market
i nd i ca to r and t o evaluate the determinants of t he NVR. The study
complemented prev ious work on t h e NVR by Smith (1974) , Rosen and
S m i t h ( l 9 8 3 ) , Gabr ie l and Nothaft (1988) and others, and r e l i e d
on the empir ica l framework l a i d ou t by these authors. Due t o the
1 i m i t a t i o n s d i scussed prev i ousl y, t he prec i s i on o f t he resu l t s
has 1 i ke ly been reduced. Therefore, caut ion should be used when
i n te rp re t i ng t h e resu l t s .
This paper suggested t h a t t h e NVR i s p re fe rab le t o the
balanced market vacancy r a t e as a market i n d i c a t o r because i t
considers var ious socio-economic var iab les and government
regu la t ions . Based on these considerat ions, t h e NVR may be useful
i n eva luat i ng, modi f y i ng o r developi ng pol i cy. Therefore, the NVR
could be used i n conjunct ion w i t h the observed vacancy ra te , t o
determi ne i f a c e r t a i n p o l i c y o r group o f po l i c i e s have s a t i s f i e d
any goals o r c r i t e r i a . I n terms of urban planning, t h e NVR, when
appl i e d t o e i t h e r speci f i c areas of a municipal i t y o r t o speci f i c
dwelling types cou ld be usefu l i n encouraging and/or p ro tec t ing
those developments t h a t help t o balance the market.
REFERENCES
8enjamin, John D., Jud, G. Donald, and Winkler, Daniel T . Re ta i l Space, Natura l Vacancy Rates, and Market Equ i l ib r ium. Unpublished paper. September 1997.
Bossons, John. 1993. "Regulation and t h e Cost o f Housingw, House, Home. and Community: Proqress i n Housinq Canadians 1945-1986, ed. John. R. Miron, 110-135. Ottawa: Canada Mortgage and Housi ng Corporat i on.
Canada Mortgage and Housing Corporat ion. 1990 t o 1996. Housing In format ion Monthly. Ottawa: Canada Mortgage and Housing Corporat i on.
. 1996. Rental Market Survev: Windsor CMA, October, 1996. Ottawa: Canada Mortgage and Housing Corporation.
Cl ayton Research Associates Limi ted . 1994%. Clayton Housi ng Report, Ju l y , 1995. Scarborough: Clayton Research Associates t i m i ted.
Denton, Frank T., Feaver, Ch r i s t i ne H., Mul ler , Andrew R., Robb, A. Les l ie , and Spencer, Bryon G. 1994. Test inq Hypotheses About Rent Contro l : F ina l Report 8 Follow-up Work Assessment. Ottawa: Canada Mortgage and Housing Corporat ion
Hendershott, P a t r i c H. and Haurin, Donald R. 1988. "Adjustments i n t h e Real Estate Marketv1, AREUEA Journal, 16: 343-353.
Ho, Lok Sang. 1992. "Rent Control : I t s Rat ionale and E f f ec t s " , Urban Studies, 29 ( 7 ) : 1183-1990.
Hubert, Franz. 1993. "The Impact o f Rent Control on Rents i n the Free Sector", Urban Studi es, 30 (1 ) : 51 -61 .
Gabr ie l , S tua r t A. and Nothaft, Frank E. 1988. "Rental Housing Markets and the Natural Vacancy Rate", AREUEA Journal , 16: 41 9-29.
Johnston, R. J. 1980. M u l t i v a r i a t e S t a t i s t i c a l Analvsi s i n Geography. New York: John Wiley & Sons.
Jud, G. Donald and Frew, James. 1990. " A t y p i c a l i t y and the Natural Vacancy ~ a t e Hypothesis", AREUEA ,Journal, 18: 294- 301.
J u d , G. Donald and Puryear, S. 1989. T r i a d Apartment Survey, Vol urnes 1988 and 1989. Greensboro P l anni ng Department and T r i a d Apartment Associat ion.
La V a l le, P. 0 . 1990. Essent ia ls o f S t a t i s t i c a l Geography: Second Ed i t ion . Dubuque, Iowa: KendaIlHunt Publ ishing Company.
Larson, Gary. 1996. Last C h a ~ t e r and Worse. Kansas C i t y : Andrews and McMeel.
Marks, Denton. l984a. ''The E f f e c t s o f P a r t i a l -Coverage Rent Contro l on t h e P r i c e and Quanti t y o f Rental Housing", Journal o f Urban Economics, 16: 360-369.
. 1984b. "The E f f e c t o f Rent Con t ro l on the P r i c e o f Rental Housi ng : An Hedoni c Approacht' , Land Economi CS, 60 ( 1 ) : 81-94.
Neter, John, Wasserman, Wi l l iam, and Whitmore, G.A. 1988. App l i ed S t a t i s t i c s : Thi r d Edi t i o n . Toronto: A l l y n and Bacon, I n c .
Powell, F.C. 1982. S t a t i s t i c a l Tables For The Socia l , B i o l o q i c a l and Physi c a l Sciences. New York: Cambridge Uni v e r s i t y Press.
Rapaport , Carol . 1992. "Rent Regul a t i on and Housi ng-Market Dynami CS", The Ameri can Economi c Revi ew, 8 2 (2 ) : 446-451 .
Read, Co l i n . 1988. "Adver t i s i ng and Na tu ra l Vacanci es i n Renta l Housing Markets", AREUEA Journal , 16: 354-363.
. 1993. V e n a n t s ' Search and Vacancies i n Rental Housing Marketst*, Regi onal Sci ence and Urban Economi CS, 23 : 171-183.
Reece, B .F . 1988. "The Price-Adjustment Process f o r Rental Housi ng: Some Fur ther Evi dence", AREUEA Journal, 16: 41 1-18,
Rosen, Kenneth T. and Smith, Lawrence 8 . 1983. "The P r i c e - Adjustment Process f o r Rental Housing and t h e Na tu ra i Vacancy Rate", The Ameri can Economi c Revi ew, 83: 779-86.
Smith, Lawrence B. 1974. "A Note on t h e P r i c e Adjustrnent Mechanism f o r Rental Housingw, The Arnerican Economic Review, 64: 478-81.
Smith, Lawrence B. and Tom1 i nson, Pe te r . 1981. "Rent Con t ro l s i n Ontar io : Roofs o r Ce i l i ngs " . AREUEA Journal , 9 ( 2 ) : 93-114.
S t a t i s t i c s Canada. 1992. The Nation, 1991. 1991 Census o f Canada. Cat. no. 93-31 1 . Ottawa: S t a t i s t i c s Canada.
. 1997. Census D ic t i ona ry , 1996. 1996 Census o f Canada. Cat . no. 92-351 -XPE. Ottawa: S t a t i s t i CS Canada.
Sternberg, Theodore 0 . 1994. "The Dura t i on o f Rental Housing Vacanci esw, Journal o f Urban Economi CS, 36: 143-1 6 0 .
U n i v e r s i t y o f Waterloo. 1998. Facts and Figures, Web S i t e http://www.adm.uwaterloo.ca/infocecs/aboutcecs.htm1.
V o i t h , R i chard and Crone, Theodore. 1988. "Nat ional Vacancy Rates and t h e Pers is tence o f Shocks i n U.S. O f f i c e Markets", AREUEA Journal , 16: 437-58.
Wheaton, W i l l i a m C. and Torto, Raymond G . 1988. "Vacancy Rates and t h e Fu tu re of O f f i c e Rentst*, AREUEA Journal, 16: 430-36.
BARRIE CA STATIST ICAL REPORT
Constant - - ..
VR
Regression Statistics
Dependent variable
Analysis of Variai F
Std. Error Std. Emor ~ F T A of Beta b of b t (6) p-teve~
0.03390 0.01 573 2.15570 - - - - _ 0.07449
' -0.051 44 0.40778 -0.00114 0.00901 9.12618 0.90371
R = 0.0514: R~ = 0.0026: Adjusted R~ = -; Durbin-Watson = 1 .O546 - - - - - - -- - -- -- -- - - . -- -- - - - - - - F(1.6) = 0.0159 p c 0.9037: Std. Emx of Estimate = 0.021 1
PCR = Percent Change in Rent
ce Sum of Mean
- - - - - - - -- a - - - - A -
Residual 0.00267-- 6 0.00~Ck4- - - - - Total 0.00267
Descriptive Statistics ,
1 Vafid N Mean Sum Minimum Maximum
GUELPH CA STATlSTlCAL REPORT
L 1
PCR - - . . - -- - . VR
PCR VR
Analysis of Variance 1 Sumof Mean
8 0.032 - - - - - -. - - . - -. - . - - - -- 0.257 - 0.006
- 0.061
9 1 -389 1 2 . 5 ~ 0. l -~ - - 3.1 00 Range Variance Std. ûev Skewness Kurtosis
0.055 0.000 0.020 0.047 - - - - - -- - - -- -
-0.132 3.000 0.884 0.940 0.321 - - O. 121
Multiple Regression Resub
Constant - - -
VR
Regression Statistlcs
Dependent Vanable
- . .. -- - * - - - - Total 0100748
Std. Error Std. Emr BETA of Beta b of b t (6) p-level
- 0.05827 - - - - . - . - 0.02289 - - 2.54620 0.04371
-0.33038- - 0.38532 -0.01 077- 0.01256 -0.85742- 0.42413
R = 0.3304; R~ = 0.1092: Adjusted R' = -; Durbin-Watson = 1 S586 - - - - . -- Ç(1.6) = 0.7352 p c 0.424t; Std. Enor of ~stirnate = 0.0333
PCR = Percent Change in Rent
Reg ression Squares df Squares F p-lever
0.00082 1 0.00082 0.7351 7 0.42413
Descriptive Statistics
PCR VR
PCR VR
Valid N Mean Sum Minimum Maximum 8 0.041 0.332 O. 009
- . - - -- - - . . - - - - - - . . - - - -- - - . 0.083 9 1.400 12.600 O. 1 O0 2.800
Range Variance Std. Dev Skewness Kurtosis - 0.075 0.001 0.033 0.496 -2.1 24
- - - -. - - - - - - - - -- - - - - - - -- 2.700 1.1 17 1 .O57 0.090 -1.621
IRegression Statistics IR = 0.6322: R~ = 0.3996; Adiusted R' = 0.2996: Durbin-Watson = 1.2442 1
Multiple Regression ResuCts
Constant - ---
VR
Std. Error Std. Enor BETA of Beta b ofb t (6) plevel
7.1 5760 1 -81 657 3.9401 7 0.00762 -- -. -- -. --A - --- -- -- - - - - - - -- --- - -
-0.6321 7 0.31632 -1.98575 0.993ô1 -1.99851 0.09262
Dependent Variable
F(1.6) = 3.9940 p c 0.0926; Std. Error of Estimate = 1.7022
PCR = Percent Change in Rent 1
Analysis of Variance
Regression Residual Total
1 ~escr i~ t ive Statistics
KINGSTON CA STATlSTlCAL REPORT
Sum of Mean Squares d f Squares F p-level
11.57326 1 11 -57326 3.99405 0.09262 - - - - * - . - - - -- . - -- -
1738576- - -- 6 2.89763 - - - - - -- - - - -- - - - - -
28.95902
PCR - - .
VR
PCR - .
VR
Valid N Mean Sum Minimum Maximum 8 0.037 0.299 0.01 3
- - - - - -- - - - - - - -. - -- -- - .- -. - - - - 0.065 9 1 .sr8 14.200 0.400 2.500
Ranae Variance Std. ûev Skewness Kurtosis - .- - 0.051
*- 0.000 - 0.020 -0.099 -1.831
-
2.100 0.562--- 0.750--- - - -0.649 -0.965
'~nalysis of Variance 1 Sumof Mean
Multiple Regression Resuîts I
Constant - - -- - - -
VR
Regression Statistics
Dependent Variable
Std. Enor Std. Enor BETA of 8eta b of b t (6) plevel
1
- - 0.06848 0.0 1065 6.42936 0.00067 - -- - - - - - - 4.79637 0.24691 -0.01 396' 0.00433 -3.22529 0.01802
R = 0.7964; R' = 0.6342: Adjusted R' = 0.5732; Durbin-Watson = 2.0877 . - - - - y - - - - - - - - - - - - -
F(1,6) = 10.4025 p c 0.0180: ~ t d r Enor of Estimate = 0.0146 J
PCR = Percent Change in Rent
Reg ression Residual Total
Squares d f Squares F p-level 0.02231 1 0.02231 10.40248 -- 0.01 802
-- - --- -.
- . 0.00129 6 0.00021 - - - - - - - - 0.02360---
Descriptive Statistics
PCR - - VR
Valid N Mean Sum Minimum Maximum 8 0.039 0.308 0.009 0.065
- - - . - - - - - - -- --- . - - - - - ---- 9 1.956 17.600 0.300 4.200
Range Variance Std. Dev Skewness Kurtosis L
PCR .
VR
- 0.056 0.001 0.022 -0.150 -1.595
- - - . - - - - - - - - -- - - - -. - - --A - -- - .- -. - - - - 3.900 1.770 1.331 0.263 9.895
KITCHENER CMA STATISTICAL REPORT
I~ul t in~e Reoression Resuits 1 Std. Enor Std. Enor
6-A of 6eta b of b t (6) p-ievel 4.88928 2.48467 1 .%778
- - - -- - -- - - -- - -"- -- -- - -- -- -- - O.OS5 - ---
4.16455 0.40568 -0.32930 0.80587 4.40862 0.697W
R = 0.1645; R' = 0.0271 : Adjusted R' = -.13Sl; Durbin-Watson = 0.931 1 - - - - - - - - - - - - - - - - - - - - - - -
F11.61 = 0.1669 D < 0.6970: Std. Enor of Estimate = 3.2334 - -
I ~ e ~ e n d e n t Variable ~PCR = Percent Change in Rent 1 - - - -
l~nalvsis of Variance 1 1 1 Sum of Mean 1
Regression Squares df Squares F p-ievel
1.74567 1 1.74567 0.1 6697 0.69700 - - - - - Residual
LONDON CMA STATISTICAL REPORT
- -- - - - &. - - - - - - - - - . - - - - -. . . 62,72936 6- ' 1 0 4 ~ 8 9 -
--. - - -- - - - - - - - - -- - - -- - - - -- - - -- - - - - -
Descriptive Statistics
Multiple Regression Resub I Std. Enor Std. Error
Total 64.47503 A
PCR . - - --
VR
PCR . - -
VR
Valid N Mean Sum Minimum Maximum 8 0.040 0.319 4.003 - -- --.- -- - -- - - -.-- 0.077 9 2.478 22.300 0 . 4 6 - - --- 4.400
Range Variance Std. ûev Skewness Kurtosis
. 0.079 -- - -. -- - - - - - 0.001 0.030 4.145 .6-1
1 - - - - - - - -- - - - -1.541 -- -
4.000 1.618 0.143- -1.676
Constant
Analysis of Variai F
BETA of Beta b of b (6) p-level 9.69369 1 .a6638 5.19384 0.00203
- - - - . -.
Regression Statistics
ice 1
Sum of Mean
--
R = 0.8227; R~ = 0.6768; Adjusted R' = 0.6230; Durbin-Watson = 2.0399 - - - - - - . - - - - - -
F(1,6) = 12.5639 p c 0.01 22; ~ t d &or of Ëstimate = 1.2194
Squares d f Squares F p-level 1 8.6821 9 1 18.68219 12.56395 0.01215
Dependent Variable ~PCR = Percent Change in Rent
tics ,
Valid N Mean Sum Minimum Maximum
- Residual Total
- - - - - - -- - - - - - - - . . . - - -
8.921 80- 6 1.48697 - - - - - - - -- - - - . . - - . - - - - - 27.60399-
- VR
- - - - - - -- - - - . - - - - - - - - - -
9 3.600 - - 32406- 2.100 5.800 Rame Variance Std. Dev Skewness Kurtosis
PCR - - -
VR
- O.Oô3 -- 0.000 0.020 -0.480 0.223
.-- - ----- ----- -- . -- - 3.700 - 1.125 1 .O61 0.834 1.699
PETERBOROUGH CA STATlSTlCAL REPORT
Multiple Regression Resuits I
I Std. Enor Std. Enor
I Regression Statistics R = 0.6279; R~ = 0.3943; Adjusted F f = 0.2934; Durbin-Watson = 1.9217 -- -- -- -- - - - - . -. . - - - - -- - -. - - --
IF(1.6) = 3.9065 D c 0.0955: Çld. Enor of Estimate = 0.0223 1 Constant
- -
VR
l~ependent Variable ~PCR = Percent Change in Rent I
BETA of üeta b of b t (6) p-level 0.07285 0.02081 3.501 07 0.01281
- - - - - - - - . --. - . - - -. - - - . - - - - . -- - . - . - - - - - -- - - - 6.62796 0.31772 4.01 119 O. 00566 -1 -97648 0.09549
Analysis of Variance 1 Sum of Mean
1 Descriptive Statistics 1
Regression - - - Residual
.
Total
Squares d f Squares F p-kvel 0.00195 1 0.001 95 3.90646 -- - - . - -- - - - - . - - - - - - - - - - - 0.09549
-
- - 0.00300 6 - - 0.00050 -
0 k 9 5 ‘ -
ST-CATHARINES-NIAGARA CMA STATlSTlCAL REPORT
PCR - - .
VR
PCR - -
VR
~ul t ip le~egres~on~ResuI ts 1 Std. Enor Std. Enor
valid N -
Mean Sum Minimum Maximum 8 -- - -- . - - - - - . - - -
0.035 -
0.278 - - - - - - -0.02 - - - - - 0.û68 9 3.256- - 29.300 1 .O00 5.400
Range Variance Std. Dev Skewness Kurtosis 0.071
- - - -. . - - . - - - 0.001 0.027 - -. - . - - - - - -- 0.101
* - - - -- -1.61 3 4.400 2.135 1.461 O. 168 -0.964
l~e~ress lon Statistia IR = 0.9572; R~ = 0.9163; Adjusted R' = 0.9024; Durbin-Watson = 1.4167 - . . . . - - - - I
Constant
VR
BETA of Beta b of b t (6) p-levei
- - - - 9.451 95 0.77081 12.26235 - - . - . - - -
0.00002
-0.95724 0.11811 -1.51472 O. 18689 -0.81050- -- 0.00019
nalysis of Variance 1 Sumof Mean
Dependent Variable
F(1.6) = 65.6912 p c 0.0002; Std. Error of Estimate = 0.8931 !
PCR = Percent Change in Rent
Reg ression Residual
. -
Total
Squares d f Squares F plevel 52.40081 1 52.40081 65.691 18 0.00019
- - -. - -.
- - 4.78610- - - 6 -- 0.79768- a -
. . -.
57.18691-
Descriptive Statistics
PCR VR
PCR -
VR
Valid N Mean Sum Minimum Maximum 8
- - - - - - -. - 0.038 -- -
0.300 --- - 0.001 - - - - - - - 0.075
9 - 3.456- 31.100 0.900 5 . 8 ~ Range Variance Std. Dev Skewness Kurtosis
0.074 0.001 0.029 0.085 - - -1.974 -
4.900 3.703 1.924-- -0.180- -1 -81 O
Multiple Regression Results I ~ t d . Error Std. Enor
Constant - -
VR
Analysis of Variance 1 Sum of Mean
BETA of Beta b of b t (6) p-level
0.08534 4.50660 0.00408 - * -
0.01 894 - -- - - .- - - - - -- - -- -
-0.74627 0.271 75 4-01 51 3 0.00551 -2.74616 0.03346
Regression Statistics
Dependent Variable
R = 0.7463; R' = 0.5569; Adjusted* = 0.4831: Duibin-Watson = 1 2471 - - - -- - - - - - -- - - -- - -- - - - - - - - - - - - - F(1.6)=7.5414 p < 0.0335; Std. Emr of Estimate = 0.031 1-
PCR = Perœnt Chanpe in Rent
DescriptiveStatistics - -
1 Valid N Mean Sum Minimum Maximum
Rwression Squares d f Squares F p-level
O. 00727 1 0.00727 7.54140 0.03346
WINDSOR CMA STATISTICAL REPORT
1 PCR
Range Variance Std. Dev Skewness Kurtosis
0.1 29 0.002 0.043 0.795 -0.037
Multiple Regression Resuits
' ~ n a l ~ s i s of Variance 1 Sumof Mean
Constant
VR
-Regression Statistics
Dependent Variable
Std. Enor Std. Enor 8ETA of &ta b of b t (6) plevel
7.1 8787 3.56821 2.01442 A - - - - - - . . -
0.04060
-0.37007- 0.37927- - -1 -53366 1.571 77 -0.97575- 0.36687 - - -
R = 0.3701; R~ = 0.1369; Adjusted R~ = -0.0069: ~ u r b i n - ~ a t s i = 1.6835 - - - - . . - . - - - - .
F(1.6) = 0.9521 p < 0.3669; Std. Ermr of Estimate = 3.071 5 4
PCR = Percent Change in Rent
Rearession Squares d f Squares F plevel
8.98208 1 8.98208 0.95209 0.36687
?
Descriptive Statistics
PCR -
VR
Valid N Mean Sum Minimum Maximum 8 0,039 0.310 0.014 0.103
. - - - - - -- - - s_l oo -- --- - 9 2.01 1 0:800- 3.000
Range Variance Std. Dev Skewness Kurtosis PCR VR
- J
0.089 0.001 0.031 1.625 2.232 ---. - -- --- ----- -
2.200 0.684 0.827 -0.205 -1.454
Constant - - -
VR - ---
Barrie CA
Guelph CA - - - - - . - --
Hamilton CMA - - -
Kingston CA
Kitchener CMA
London CG - -
Peterborough CA -. .
st.~atharines CMA
Sudbury CMA
Multiple Reglession Resub 1
r
F F . r F -
1 Regression statistio
Std. Enor Std. Enor BETA of Bcta b of b t (69) plevel
- - --- - - - -- . - O.il6538 0.00938 6.96570 . - - - - - - - - - - - - - - - - - - - - - .- - - - 0 . m
- --
-0.68924 0.1 1692 9.01 233 0.00209 -5.89478 - 0.0000(1 -- -- - --A-- --- -- - -- - -- - - -- - - - . - -- -
-0.160% 0.13149 -0.01426 0.01 170 -1.21961 0.22673 - - - - - &. A -. - -- - - - - - - - -- -- -- -- - - - -. - - - - - - - - - - - - - - - - . - -
-0.05244 0.13142 -0.00467 0.01 469 -0.39904 0.691 09 - - - - - -. - - --- -- -
4.07630 0.1 31 07 -0.00679 0.01 166 -0.58214 0.56237 - - - ----- -
-0.00459 - - - - -
0.13066 -0.00041 0.01 162 -0.0351 3 0.97200 -- -- - - - - -- - - - - - - -- - - -- - -- - -- - - - - - - - 0.09282 0.1 31 36 0.00826 0.01 168
- - - - - - - - - - - - - * 0.70657 0.48221
0.1 5623 0.1 3614 0.01 390 0.0121 1 1.14761- - 0.%09 - - - A - - - - - - - -. - - - -- -- - -- - - - . - - - - - - -. - .
-- 0.1 2762 0.13387 0.01 135 0.01 1@ - - - - - . - - - - --- - - - A - - - -. . - . - -- 0.95331 0.34376
0 . 2 0 8 g - -
O. 13597 0.01 855 0.01209 1.53338- -
--LA----.---- - - - - - -
0.12976
O. 1 3622 0.13:52 0.01212 0.01 170 1 .O3573 0.30%
Z = 0.5878: R' = 0.3455; Adjusted R' = 0.2507; Durttin-Watson = 1.7130 - -- - - . -- - - - - -- -
:(10.69) = 36431 p c 0.0006; Std. ~ k r of Estirnate = 0.0232 - -
CR = Percent Change in Rent
Analysis of Variance 1 Sum of Mean
1 1 aua ares d f Sauares F wlevel 1 Regression 1 0.01968 10 0.00197 3.64307 0.0061 81
- Correlation Matrix 1 Variable I' VR PCR 1
I Hamilton CMA
Kingston CA I I Kitchener CMA
London CMA
Sudbury CMA l+l
Please note: Correlation between al1
durnmy variables was -0.1 1.
Descriptive Statistics 1 ValidN Mean Sum Minimum Maximum
PCR - -.
VR
PCR - - . -
VR
80 0.03759 3.60680 -0.00694 . --- . - -- - - -- ---- - - - -- - -
0.12180
80 2.56250 205.00000 0.1 0000 5.-kOilC
Range Variance Std. Dev Skewness Kurtosis
O. 1 2873 0.00721 0.02685 0.59215 -0.02642 -- ---- ---- -
5.80000 2.25250 1.50083 0.44663 -0.53436
NATURAL VACANCY RATE STATlSTlCAL REPORT
' ~ u l t i ~ l e Regression Result. 1 ~ t d . Enor Std. Error
'constant
RNT -
cSH - -
CPOP
MOB
BETA of Beta b ofb t (4) p-kvel
- --- 12.5309 10.9920 1.1400 0.3179
- - - - A . - - - - *
4.3376 0.6336 4.0093 0.01 74 -0.5327 0.62î4 --- -- - - - -1 -3050 -
0.9907 O. 0000 0.0000 -1.31 73 0.2581 . - - - - - - - -- ---- - - -
2.9470~- ' 1.6874 0.0021 0.0012 1.7465 O. 1557 - - - -- - - -- - - A A- -- - - - - -- - - -- - - -- - - - - -
-1 -9340 1.3198 -0.0007 0.0005 -1.4654 0.2167 - - - - -- -- - - -- -- ----- -- ---
-0.3813 0.4363 46.71 37 53.451 1 -0.8740 0.431 5 -- -
Regression Statistics
Dependent Variable
- - - - - - -
R = 0.8096; R* = 0.6554: Adjusteci R' = 0248; Durbin-Watson = 1.0396 - -- - - - - -- - - - - - - - - - - . - -
F(5.4) = 1 S221 p < 0.3525: Std. Enor of Estimate = 0.7714 1
W R = Natural Vacancv Rate
Analysis of Variance 1 Sumof Mean
l~orrelation Matrix 1
Regression - -
Residual - - - _ Total
\variable 1 R M PS cSH cPOP MOB NVR 1
Squares d f Squares F pkvel 4.5285 5 0.9057 1 S221 0.3525
, - -. . - - - - - - - - - - - . - -- - - - - - - -- - -- -- - - 2.3801 4 0.5950 _ _ _ - - _ - - - _.- -- - - - _ _ _ ---, 6.9085
1 Descriotive Statktics 1
RNT
PS
cSH - . --
cPOP
MOB - - - -
NVR
RNT PS cSH cPOP MO5 NVR
1-00 -0.66 -0.54 -0.27 -0.19 - - - - - - - - - -- - - - - A - - - - - - ---.. *-
-0.49 -- -
- - -0.66 - -.-. - 1 .O0 009 0.74 - - - - 4.07 -- -
0.15
. - -0.54
-.-- 089-
- . - - -- - - - 1%-- - - 0.93 0.20
- - - 0.10
-0.27 0.74 0.93 1 .O0 - - - - - - - - - - - 0.27 - - --
-0.17
. - - 4-19 -- -- -- -0.07
- - - 0:20- - - - 0.27 1 .O0 -0.16 - - - . - - - - - - - -
-0.49 0.15 011 O -0.17 -0.16 1 .O0
RNT - - - - FS c S H cPOP MOB NVR
Valid N Mean SUIG Minimum Maximum 1
- - . - - -. - - -- 10 3636.500--36365.000- - -- . - - - - ~ll6l.OOO -- - 7175:~~ -
10- 0.034 0.344 0.024 0.045 Ranae Vananœ Std. Dev Skewness Kurtosis
NAME:
PLACE OF BIRTH:
YEAR OF BIRTH:
EDUCATION:
Adam Mark Szymczak
Toronto, Ontario, Canada
St. John's College, Brantford, Ontario 1981-1986
University of Toronto, Toronto, Ontario 1 986-1 990 B.A. Emnomics 8 Geography
University of Waterloo, Waterloo, Ontario 1990 Post-Degree Studies
University of Windsor, Windsor, Ontario 1994-1 998 MA. Geography (Urban Planning)