Melbourne Housing Price Analysis
Sharmistha Sutradhar
January 26, 2018
1. Read dataset.
MHousing.df <- read.csv("C:/interships/Melbourne_housing_FULL.csv")
View(MHousing.df)2. Length and breadth of the dataset.
dim(MHousing.df)## [1] 30931 213. Descriptive statistic of each variable.
library("psych", lib.loc="~/R/win-library/3.4")
describe(MHousing.df[,c(3,5,9,11:15)])## vars n mean sd median trimmed mad
## Rooms 1 30931 3.02 0.97 3 2.99 1.48
## Price 2 24197 1052590.44 644499.49 875000 953681.82 437367.00
## Distance* 3 30931 117.62 77.58 101 117.00 109.71
## Bedroom2 4 23868 3.05 0.99 3 3.02 1.48
## Bathroom 5 23862 1.61 0.72 1 1.50 1.48
## Car 6 23497 1.70 1.00 2 1.61 1.48
## Landsize 7 20405 587.10 3540.11 500 451.57 329.14
## BuildingArea 8 12397 158.34 418.09 133 141.88 59.30
## min max range skew kurtosis se
## Rooms 1 16 15 0.54 2.75 0.01
## Price 85000 11200000 11115000 2.59 13.33 4143.26
## Distance* 1 232 231 0.09 -1.62 0.44
## Bedroom2 0 30 30 1.52 28.69 0.01
## Bathroom 0 12 12 1.42 5.30 0.00
## Car 0 26 26 2.14 22.71 0.01
## Landsize 0 433014 433014 95.26 11096.44 24.78
## BuildingArea 0 44515 44515 96.90 10223.76 3.764. One-way contingency tables for the categorical variables in the dataset.
one-way table for the variable: Suburb.
table(MHousing.df$Suburb)##
## Abbotsford Aberfeldie Airport West
## 131 75 147
## Albanvale Albert Park Albion
## 12 125 64
## Alphington Altona Altona Meadows
## 69 115 26
## Altona North Ardeer Armadale
## 154 24 215
## Ascot Vale Ashburton Ashwood
## 227 147 126
## Aspendale Aspendale Gardens Attwood
## 35 15 12
## Avondale Heights Avonsleigh Bacchus Marsh
## 150 1 4
## Balaclava Balwyn Balwyn North
## 55 299 381
## Bayswater Bayswater North Beaconsfield
## 40 21 6
## Beaconsfield Upper Beaumaris Belgrave
## 2 77 1
## Bellfield Bentleigh Bentleigh East
## 32 284 536
## Berwick Black Rock Blackburn
## 41 38 69
## Blackburn North Blackburn South Bonbeach
## 45 47 15
## Boronia Botanic Ridge Box Hill
## 42 1 108
## Braybrook Briar Hill Brighton
## 88 23 426
## Brighton East Broadmeadows Brookfield
## 359 70 4
## Brooklyn Brunswick Brunswick East
## 39 402 173
## Brunswick West Bulla Bulleen
## 217 1 140
## Bullengarook Bundoora Burnley
## 4 143 11
## Burnside Burnside Heights Burwood
## 11 11 220
## Burwood East Cairnlea Camberwell
## 46 25 371
## Campbellfield Canterbury Carlton
## 16 130 89
## Carlton North Carnegie Caroline Springs
## 88 258 22
## Carrum Carrum Downs Caulfield
## 22 21 21
## Caulfield East Caulfield North Caulfield South
## 23 78 108
## Chadstone Chelsea Chelsea Heights
## 113 36 18
## Cheltenham Chirnside Park Clarinda
## 127 9 19
## Clayton Clayton South Clifton Hill
## 48 39 128
## Clyde North Coburg Coburg North
## 3 369 139
## Coldstream Collingwood Coolaroo
## 1 117 8
## Craigieburn Cranbourne Cranbourne East
## 193 11 1
## Cranbourne North Cranbourne West Cremorne
## 5 3 42
## croydon Croydon Croydon Hills
## 1 107 12
## Croydon North Croydon South Dallas
## 26 7 30
## Dandenong Dandenong North Darley
## 49 58 2
## Deepdene Deer Park Delahey
## 10 40 20
## Derrimut Diamond Creek Diggers Rest
## 13 25 7
## Dingley Village Docklands Doncaster
## 42 15 240
## Doncaster East Donvale Doreen
## 126 46 25
## Doveton Eaglemont East Melbourne
## 22 55 55
## Edithvale Elsternwick Eltham
## 34 120 74
## Eltham North Elwood Emerald
## 26 264 3
## Endeavour Hills Epping Essendon
## 21 136 367
## Essendon North Essendon West Eumemmerring
## 44 42 3
## Eynesbury Fairfield Fawkner
## 1 91 183
## Fawkner Lot Ferntree Gully Fitzroy
## 1 54 140
## Fitzroy North Flemington Footscray
## 168 103 243
## Forest Hill Frankston Frankston North
## 61 108 15
## Frankston South Gardenvale Gisborne
## 45 13 21
## Gisborne South Gladstone Park Glen Huntly
## 2 45 52
## Glen Iris Glen Waverley Glenroy
## 455 196 374
## Gowanbrae Greensborough Greenvale
## 43 115 67
## Guys Hill Hadfield Hallam
## 1 96 10
## Hampton Hampton East Hampton Park
## 258 107 7
## Hawthorn Hawthorn East Healesville
## 381 261 2
## Heatherton Heathmont Heidelberg
## 2 61 101
## Heidelberg Heights Heidelberg West Highett
## 144 120 92
## Hillside Hopetoun Park Hoppers Crossing
## 57 1 149
## Hughesdale Huntingdale Hurstbridge
## 73 11 2
## Ivanhoe Ivanhoe East Jacana
## 216 54 47
## Kealba Keilor Keilor Downs
## 35 20 37
## Keilor East Keilor Lodge Keilor Park
## 211 5 37
## Kensington Kew Kew East
## 187 423 119
## Keysborough Kilsyth Kings Park
## 37 18 21
## Kingsbury Kingsville Knoxfield
## 33 69 14
## Kooyong Kurunjang Lalor
## 16 15 90
## Langwarrin Laverton Lilydale
## 17 5 4
## Lower Plenty Lynbrook Lysterfield
## 20 2 1
## MacLeod Maidstone Malvern
## 64 167 206
## Malvern East Maribyrnong McKinnon
## 330 214 40
## Meadow Heights Melbourne Melton
## 42 190 33
## Melton South Melton West Mentone
## 51 32 87
## Mernda Mickleham Middle Park
## 67 8 67
## Mill Park Mitcham Monbulk
## 136 94 1
## Mont Albert Montmorency Montrose
## 99 50 2
## Moonee Ponds Moorabbin Mooroolbark
## 254 110 49
## Mordialloc Mount Evelyn Mount Waverley
## 54 3 202
## Mulgrave Murrumbeena Narre Warren
## 80 158 20
## New Gisborne Newport Niddrie
## 6 223 131
## Noble Park North Melbourne North Warrandyte
## 70 142 5
## Northcote Notting Hill Nunawading
## 377 10 58
## Oak Park Oakleigh Oakleigh East
## 123 99 26
## Oakleigh South Officer Olinda
## 132 4 1
## Ormond Pakenham Parkdale
## 143 16 76
## Parkville Pascoe Vale Patterson Lakes
## 56 347 5
## Plenty Plumpton Point Cook
## 2 6 91
## Port Melbourne Prahran Preston
## 330 311 438
## Princes Hill Research Reservoir
## 21 4 773
## Richmond Riddells Creek Ringwood
## 511 6 75
## Ringwood East Ringwood North Ripponlea
## 46 28 16
## Rockbank Rosanna Rowville
## 3 157 34
## Roxburgh Park Sandhurst Sandringham
## 71 3 80
## Scoresby Seabrook Seaford
## 14 7 37
## Seaholme Seddon Silvan
## 17 102 2
## Skye South Kingsville South Melbourne
## 4 37 172
## South Morang South Yarra Southbank
## 94 397 67
## Spotswood Springvale Springvale South
## 51 39 14
## St Albans St Helena St Kilda
## 120 10 346
## Strathmore Strathmore Heights Sunbury
## 154 12 123
## Sunshine Sunshine North Sunshine West
## 181 137 182
## Surrey Hills Sydenham Tarneit
## 270 23 43
## Taylors Hill Taylors Lakes Tecoma
## 27 51 2
## Templestowe Templestowe Lower The Basin
## 76 194 4
## Thomastown Thornbury Toorak
## 85 281 259
## Travancore Truganina Tullamarine
## 16 19 47
## Upwey Vermont Vermont South
## 1 38 42
## viewbank Viewbank Wallan
## 1 94 13
## Wantirna Wantirna South Warrandyte
## 41 44 12
## Warranwood Waterways Watsonia
## 3 5 91
## Watsonia North Wattle Glen Werribee
## 25 2 139
## Werribee South West Footscray West Melbourne
## 1 183 63
## Westmeadows Wheelers Hill Whittlesea
## 40 76 4
## Wildwood Williams Landing Williamstown
## 1 6 187
## Williamstown North Windsor Wollert
## 28 120 48
## Wonga Park Wyndham Vale Yallambie
## 3 59 47
## Yarra Glen Yarraville
## 1 276one-way table for the variable: Type.
table23<-table(MHousing.df$Type)
table23##
## h t u
## 21160 3178 6593prop.table(table23)*100##
## h t u
## 68.41033 10.27448 21.31519one-way table for the variable: Method.
table52<-table(MHousing.df$Method)
table52##
## PI PN S SA SN SP SS VB W
## 4215 264 17687 202 1223 4503 31 2656 150prop.table(table52)*100##
## PI PN S SA SN SP
## 13.6271055 0.8535127 57.1821150 0.6530665 3.9539620 14.5582102
## SS VB W
## 0.1002231 8.5868546 0.4849504one-way table for the variable: SellarG.
table34<-table(MHousing.df$SellerG)
table34##
## @Realty A
## 9 1
## Abercromby's Ace
## 45 4
## AIME Airport
## 1 1
## Alex Alexkarbon
## 1 67
## Allan Allens
## 1 5
## Anderson Appleby
## 9 8
## Aquire Area
## 7 6
## Ascend Ash
## 4 3
## ASL Asset
## 5 6
## Assisi Aumeca
## 5 1
## Australian Avion
## 2 3
## Barlow Barry
## 52 2832
## Batty Bayside
## 1 8
## Bekdon Beller
## 23 31
## Bells Benchmark
## 79 1
## Benlor Besser
## 2 5
## Better Biggin
## 1 818
## Black Blackbird
## 1 2
## Blue Bombay
## 1 1
## Boran Boutique
## 2 5
## Bowman Brace
## 7 16
## Brad Bradly
## 635 1
## Branon Buckingham
## 7 102
## Bullen Burnham
## 4 59
## Burns Bustin
## 1 1
## Buxton Buxton/Advantage
## 1644 2
## Buxton/Buxton Buxton/Find
## 1 1
## Buxton/Marshall buyMyplace
## 2 10
## C21 Caine
## 145 39
## Calder Can
## 13 1
## CarlinSmith Carter
## 1 26
## Castran CASTRAN
## 30 1
## Cayzer Century
## 91 3
## Chambers Changing
## 16 3
## Charlton Charter
## 25 2
## Chisholm Christopher
## 153 11
## Christou Clairmont
## 1 1
## Collings Collins
## 8 115
## Community Compton
## 5 14
## Conquest Considine
## 1 54
## Cooper Coventry
## 1 1
## Craig Crane
## 4 4
## D'Aprano Daniel
## 9 7
## Darras Darren
## 3 95
## David Del
## 3 19
## Dingle Direct
## 51 1
## Dixon Domain
## 6 16
## Donovan Douglas
## 1 155
## Edward Elite
## 55 4
## Emerson Eric
## 3 3
## Eview First
## 111 8
## Flannagan Fletchers
## 12 785
## Fletchers/Fletchers Fletchers/One
## 3 1
## FN Follett
## 55 4
## For Foxtons
## 1 1
## Frank Free
## 72 1
## Galldon Gardiner
## 3 6
## Garvey Gary
## 12 375
## Gellibrand Geoff
## 1 1
## GL Grant's
## 31 2
## Grantham Greg
## 3 455
## Gunn&Co H
## 22 1
## Hall Ham
## 13 9
## Hamilton HAR
## 4 237
## Harcourts Harcourts/Barry
## 381 1
## Harrington Haughton
## 32 39
## Hayeswinckle hockingstuart
## 2 2405
## hockingstuart/Advantage hockingstuart/Barry
## 2 1
## hockingstuart/Buxton hockingstuart/Harcourts
## 1 1
## hockingstuart/hockingstuart hockingstuart/Jellis
## 7 1
## hockingstuart/Sweeney hockingstuart/Village
## 1 2
## Hodges Holland
## 370 11
## Home Homes
## 2 3
## Hooper Hoskins
## 2 12
## Hunter Icon
## 20 1
## Iconek iHomes
## 30 1
## Inner iOne
## 2 1
## iProperty iSell
## 1 31
## iTRAK J
## 31 33
## Jas Jason
## 415 39
## Jeffrey Jellis
## 1 2969
## Jim JMRE
## 2 6
## Joe John
## 1 1
## Johnston Joseph
## 2 3
## JRW Just
## 4 2
## Justin JY
## 2 2
## K.R.Peters Karen
## 1 1
## Kay Kaye
## 324 2
## Keatings Kelly
## 3 6
## Ken Knight
## 3 1
## L Landfield
## 9 1
## Langwell Le
## 4 7
## Leaders Leading
## 2 8
## Leased Leeburn
## 4 13
## Leyton Lindellas
## 4 32
## LITTLE LJ
## 67 84
## LJH LLC
## 4 10
## Love Lucas
## 234 7
## Luxe Luxton
## 1 2
## M.J Maddison
## 3 5
## Maitland Malvern
## 5 1
## Mandy Mark
## 5 1
## Marshall Marvelli
## 1835 1
## Mason Matthew
## 12 5
## Max McDonald
## 14 75
## McEwing McGrath
## 1 510
## McGrath/Langwell McLennan
## 1 15
## McNaughton Meadows
## 3 2
## Meallin Melbourne
## 1 26
## Metro MICM
## 3 45
## Miles Millership
## 444 26
## Mindacom Mitchell
## 2 1
## MJ Moonee
## 8 71
## Morleys Morrison
## 15 96
## MSM Munn
## 2 2
## Naison Nardella
## 1 4
## Nelson New
## 2934 6
## Nguyen Nicholas
## 7 1
## Nicholls Nicholson
## 4 14
## Nick Noel
## 98 455
## North O'Brien
## 1 179
## O'Donoghues Oak
## 11 1
## Obrien OBrien
## 17 4
## One Only
## 18 2
## Open Oriental
## 1 4
## Owen P
## 4 1
## Pagan Parkes
## 53 24
## Parkinson Paul
## 3 19
## Pavilion Peake
## 1 5
## Peninsula Peter
## 1 59
## Philip Point
## 143 10
## PRD PRDNationwide
## 2 20
## Pride Prime
## 16 1
## Private/Tiernan's Prof.
## 1 44
## Professionals Property
## 3 6
## Propertyau Prowse
## 1 2
## Purplebricks Quinta
## 149 1
## R&H Raine
## 9 279
## Raine&Horne Ray
## 5 1695
## Rayner Re
## 2 12
## RE Reach
## 14 2
## Real Red
## 3 7
## Redina Reed
## 3 1
## Reliance REMAX
## 41 7
## Rendina Rexhepi
## 114 2
## Ristic Rodney
## 5 28
## Roger Rombotis
## 1 3
## Rosin Ross
## 1 13
## Rounds Royston
## 7 1
## RT RW
## 480 235
## Ryder S&L
## 12 30
## Sanctuary Sandhurst
## 1 1
## Schroeder Scott
## 12 6
## Sell Shape
## 7 6
## Silver Smart
## 1 4
## SN Sotheby's
## 1 17
## Space Spencer
## 2 1
## Steller Sterling
## 8 1
## Steveway Stockdale
## 1 360
## Surreal Sutherland
## 2 1
## Sweeney Sweeney/Advantage
## 385 2
## Sweeney/Burnham T
## 1 1
## Tanner The
## 1 18
## Thomas Thomson
## 8 96
## Tiernan's Tim
## 1 27
## Trimson Triwest
## 16 15
## TRUE U
## 4 4
## Upper Upside
## 1 4
## Veitch Vic
## 1 1
## VicHomes Vicprop
## 1 1
## VICProp VICPROP
## 4 6
## Victory Village
## 1 200
## W.B. Walsh
## 16 5
## Walshe Waterfront
## 52 1
## Weast Weda
## 1 7
## WeSell Weston
## 1 3
## Westside White
## 12 2
## WHITEFOX Whiting
## 6 33
## William Williams
## 26 180
## Wilson Win
## 37 24
## Wood Woodards
## 2 644
## Wyndham Xynergy
## 1 3
## YPA Zahn
## 373 1prop.table(table34)*100##
## @Realty A
## 0.029097022 0.003233002
## Abercromby's Ace
## 0.145485112 0.012932010
## AIME Airport
## 0.003233002 0.003233002
## Alex Alexkarbon
## 0.003233002 0.216611167
## Allan Allens
## 0.003233002 0.016165012
## Anderson Appleby
## 0.029097022 0.025864020
## Aquire Area
## 0.022631017 0.019398015
## Ascend Ash
## 0.012932010 0.009699007
## ASL Asset
## 0.016165012 0.019398015
## Assisi Aumeca
## 0.016165012 0.003233002
## Australian Avion
## 0.006466005 0.009699007
## Barlow Barry
## 0.168116129 9.155863050
## Batty Bayside
## 0.003233002 0.025864020
## Bekdon Beller
## 0.074359057 0.100223077
## Bells Benchmark
## 0.255407197 0.003233002
## Benlor Besser
## 0.006466005 0.016165012
## Better Biggin
## 0.003233002 2.644596036
## Black Blackbird
## 0.003233002 0.006466005
## Blue Bombay
## 0.003233002 0.003233002
## Boran Boutique
## 0.006466005 0.016165012
## Bowman Brace
## 0.022631017 0.051728040
## Brad Bradly
## 2.052956581 0.003233002
## Branon Buckingham
## 0.022631017 0.329766254
## Bullen Burnham
## 0.012932010 0.190747147
## Burns Bustin
## 0.003233002 0.003233002
## Buxton Buxton/Advantage
## 5.315056093 0.006466005
## Buxton/Buxton Buxton/Find
## 0.003233002 0.003233002
## Buxton/Marshall buyMyplace
## 0.006466005 0.032330025
## C21 Caine
## 0.468785361 0.126087097
## Calder Can
## 0.042029032 0.003233002
## CarlinSmith Carter
## 0.003233002 0.084058065
## Castran CASTRAN
## 0.096990075 0.003233002
## Cayzer Century
## 0.294203227 0.009699007
## Chambers Changing
## 0.051728040 0.009699007
## Charlton Charter
## 0.080825062 0.006466005
## Chisholm Christopher
## 0.494649381 0.035563027
## Christou Clairmont
## 0.003233002 0.003233002
## Collings Collins
## 0.025864020 0.371795286
## Community Compton
## 0.016165012 0.045262035
## Conquest Considine
## 0.003233002 0.174582134
## Cooper Coventry
## 0.003233002 0.003233002
## Craig Crane
## 0.012932010 0.012932010
## D'Aprano Daniel
## 0.029097022 0.022631017
## Darras Darren
## 0.009699007 0.307135236
## David Del
## 0.009699007 0.061427047
## Dingle Direct
## 0.164883127 0.003233002
## Dixon Domain
## 0.019398015 0.051728040
## Donovan Douglas
## 0.003233002 0.501115386
## Edward Elite
## 0.177815137 0.012932010
## Emerson Eric
## 0.009699007 0.009699007
## Eview First
## 0.358863276 0.025864020
## Flannagan Fletchers
## 0.038796030 2.537906954
## Fletchers/Fletchers Fletchers/One
## 0.009699007 0.003233002
## FN Follett
## 0.177815137 0.012932010
## For Foxtons
## 0.003233002 0.003233002
## Frank Free
## 0.232776179 0.003233002
## Galldon Gardiner
## 0.009699007 0.019398015
## Garvey Gary
## 0.038796030 1.212375934
## Gellibrand Geoff
## 0.003233002 0.003233002
## GL Grant's
## 0.100223077 0.006466005
## Grantham Greg
## 0.009699007 1.471016133
## Gunn&Co H
## 0.071126055 0.003233002
## Hall Ham
## 0.042029032 0.029097022
## Hamilton HAR
## 0.012932010 0.766221590
## Harcourts Harcourts/Barry
## 1.231773948 0.003233002
## Harrington Haughton
## 0.103456080 0.126087097
## Hayeswinckle hockingstuart
## 0.006466005 7.775370987
## hockingstuart/Advantage hockingstuart/Barry
## 0.006466005 0.003233002
## hockingstuart/Buxton hockingstuart/Harcourts
## 0.003233002 0.003233002
## hockingstuart/hockingstuart hockingstuart/Jellis
## 0.022631017 0.003233002
## hockingstuart/Sweeney hockingstuart/Village
## 0.003233002 0.006466005
## Hodges Holland
## 1.196210921 0.035563027
## Home Homes
## 0.006466005 0.009699007
## Hooper Hoskins
## 0.006466005 0.038796030
## Hunter Icon
## 0.064660050 0.003233002
## Iconek iHomes
## 0.096990075 0.003233002
## Inner iOne
## 0.006466005 0.003233002
## iProperty iSell
## 0.003233002 0.100223077
## iTRAK J
## 0.100223077 0.106689082
## Jas Jason
## 1.341696033 0.126087097
## Jeffrey Jellis
## 0.003233002 9.598784391
## Jim JMRE
## 0.006466005 0.019398015
## Joe John
## 0.003233002 0.003233002
## Johnston Joseph
## 0.006466005 0.009699007
## JRW Just
## 0.012932010 0.006466005
## Justin JY
## 0.006466005 0.006466005
## K.R.Peters Karen
## 0.003233002 0.003233002
## Kay Kaye
## 1.047492807 0.006466005
## Keatings Kelly
## 0.009699007 0.019398015
## Ken Knight
## 0.009699007 0.003233002
## L Landfield
## 0.029097022 0.003233002
## Langwell Le
## 0.012932010 0.022631017
## Leaders Leading
## 0.006466005 0.025864020
## Leased Leeburn
## 0.012932010 0.042029032
## Leyton Lindellas
## 0.012932010 0.103456080
## LITTLE LJ
## 0.216611167 0.271572209
## LJH LLC
## 0.012932010 0.032330025
## Love Lucas
## 0.756522583 0.022631017
## Luxe Luxton
## 0.003233002 0.006466005
## M.J Maddison
## 0.009699007 0.016165012
## Maitland Malvern
## 0.016165012 0.003233002
## Mandy Mark
## 0.016165012 0.003233002
## Marshall Marvelli
## 5.932559568 0.003233002
## Mason Matthew
## 0.038796030 0.016165012
## Max McDonald
## 0.045262035 0.242475187
## McEwing McGrath
## 0.003233002 1.648831270
## McGrath/Langwell McLennan
## 0.003233002 0.048495037
## McNaughton Meadows
## 0.009699007 0.006466005
## Meallin Melbourne
## 0.003233002 0.084058065
## Metro MICM
## 0.009699007 0.145485112
## Miles Millership
## 1.435453105 0.084058065
## Mindacom Mitchell
## 0.006466005 0.003233002
## MJ Moonee
## 0.025864020 0.229543177
## Morleys Morrison
## 0.048495037 0.310368239
## MSM Munn
## 0.006466005 0.006466005
## Naison Nardella
## 0.003233002 0.012932010
## Nelson New
## 9.485629304 0.019398015
## Nguyen Nicholas
## 0.022631017 0.003233002
## Nicholls Nicholson
## 0.012932010 0.045262035
## Nick Noel
## 0.316834244 1.471016133
## North O'Brien
## 0.003233002 0.578707446
## O'Donoghues Oak
## 0.035563027 0.003233002
## Obrien OBrien
## 0.054961042 0.012932010
## One Only
## 0.058194045 0.006466005
## Open Oriental
## 0.003233002 0.012932010
## Owen P
## 0.012932010 0.003233002
## Pagan Parkes
## 0.171349132 0.077592060
## Parkinson Paul
## 0.009699007 0.061427047
## Pavilion Peake
## 0.003233002 0.016165012
## Peninsula Peter
## 0.003233002 0.190747147
## Philip Point
## 0.462319356 0.032330025
## PRD PRDNationwide
## 0.006466005 0.064660050
## Pride Prime
## 0.051728040 0.003233002
## Private/Tiernan's Prof.
## 0.003233002 0.142252110
## Professionals Property
## 0.009699007 0.019398015
## Propertyau Prowse
## 0.003233002 0.006466005
## Purplebricks Quinta
## 0.481717371 0.003233002
## R&H Raine
## 0.029097022 0.902007695
## Raine&Horne Ray
## 0.016165012 5.479939220
## Rayner Re
## 0.006466005 0.038796030
## RE Reach
## 0.045262035 0.006466005
## Real Red
## 0.009699007 0.022631017
## Redina Reed
## 0.009699007 0.003233002
## Reliance REMAX
## 0.132553102 0.022631017
## Rendina Rexhepi
## 0.368562284 0.006466005
## Ristic Rodney
## 0.016165012 0.090524070
## Roger Rombotis
## 0.003233002 0.009699007
## Rosin Ross
## 0.003233002 0.042029032
## Rounds Royston
## 0.022631017 0.003233002
## RT RW
## 1.551841195 0.759755585
## Ryder S&L
## 0.038796030 0.096990075
## Sanctuary Sandhurst
## 0.003233002 0.003233002
## Schroeder Scott
## 0.038796030 0.019398015
## Sell Shape
## 0.022631017 0.019398015
## Silver Smart
## 0.003233002 0.012932010
## SN Sotheby's
## 0.003233002 0.054961042
## Space Spencer
## 0.006466005 0.003233002
## Steller Sterling
## 0.025864020 0.003233002
## Steveway Stockdale
## 0.003233002 1.163880896
## Surreal Sutherland
## 0.006466005 0.003233002
## Sweeney Sweeney/Advantage
## 1.244705958 0.006466005
## Sweeney/Burnham T
## 0.003233002 0.003233002
## Tanner The
## 0.003233002 0.058194045
## Thomas Thomson
## 0.025864020 0.310368239
## Tiernan's Tim
## 0.003233002 0.087291067
## Trimson Triwest
## 0.051728040 0.048495037
## TRUE U
## 0.012932010 0.012932010
## Upper Upside
## 0.003233002 0.012932010
## Veitch Vic
## 0.003233002 0.003233002
## VicHomes Vicprop
## 0.003233002 0.003233002
## VICProp VICPROP
## 0.012932010 0.019398015
## Victory Village
## 0.003233002 0.646600498
## W.B. Walsh
## 0.051728040 0.016165012
## Walshe Waterfront
## 0.168116129 0.003233002
## Weast Weda
## 0.003233002 0.022631017
## WeSell Weston
## 0.003233002 0.009699007
## Westside White
## 0.038796030 0.006466005
## WHITEFOX Whiting
## 0.019398015 0.106689082
## William Williams
## 0.084058065 0.581940448
## Wilson Win
## 0.119621092 0.077592060
## Wood Woodards
## 0.006466005 2.082053603
## Wyndham Xynergy
## 0.003233002 0.009699007
## YPA Zahn
## 1.205909929 0.003233002one-way table for the variable: Postcode.
table59<-table(MHousing.df$Postcode)
table59##
## #N/A 3000 3002 3003 3006 3008 3011 3012 3013 3015 3016 3018 3019 3020 3021
## 1 190 55 63 67 15 345 458 276 311 215 132 88 564 188
## 3022 3023 3024 3025 3027 3028 3029 3030 3031 3032 3033 3034 3036 3037 3038
## 24 109 59 154 6 38 211 244 290 457 211 150 20 127 93
## 3039 3040 3041 3042 3043 3044 3046 3047 3048 3049 3051 3052 3053 3054 3055
## 254 484 210 315 135 347 593 147 50 52 142 56 89 109 217
## 3056 3057 3058 3059 3060 3061 3064 3065 3066 3067 3068 3070 3071 3072 3073
## 402 173 508 67 183 16 272 140 117 131 296 377 281 438 773
## 3074 3075 3076 3078 3079 3081 3082 3083 3084 3085 3087 3088 3089 3090 3093
## 85 90 136 160 270 296 136 176 408 111 116 148 25 2 20
## 3094 3095 3096 3099 3101 3102 3103 3104 3105 3106 3107 3108 3109 3111 3113
## 50 104 2 2 423 119 309 381 140 76 194 240 126 46 17
## 3115 3116 3121 3122 3123 3124 3125 3126 3127 3128 3130 3131 3132 3133 3134
## 3 9 564 381 261 371 220 130 369 108 161 119 94 80 106
## 3135 3136 3137 3138 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150
## 107 153 18 49 4 397 259 215 222 353 455 273 113 202 272
## 3151 3152 3153 3154 3155 3156 3158 3160 3161 3162 3163 3165 3166 3167 3168
## 46 85 61 4 42 55 1 3 78 129 468 536 209 132 58
## 3169 3170 3171 3172 3173 3174 3175 3177 3178 3179 3180 3181 3182 3183 3184
## 58 80 39 56 37 70 107 25 34 14 14 431 346 55 264
## 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199
## 149 426 359 365 110 92 80 127 115 87 185 103 27 37 153
## 3200 3201 3202 3204 3205 3206 3207 3335 3337 3338 3340 3427 3428 3429 3431
## 15 21 2 467 172 192 330 9 80 56 7 7 1 124 6
## 3437 3438 3750 3752 3754 3756 3757 3765 3770 3775 3777 3782 3788 3793 3795
## 27 6 48 94 92 13 4 2 1 1 2 4 1 1 2
## 3796 3802 3803 3805 3806 3807 3808 3809 3810 3910 3975 3976 3977 3978
## 3 21 10 20 41 7 2 4 16 17 2 7 28 3prop.table(table59)*100##
## #N/A 3000 3002 3003 3006 3008
## 0.003233002 0.614270473 0.177815137 0.203679157 0.216611167 0.048495037
## 3011 3012 3013 3015 3016 3018
## 1.115385859 1.480715140 0.892308687 1.005463774 0.695095535 0.426756329
## 3019 3020 3021 3022 3023 3024
## 0.284504219 1.823413404 0.607804468 0.077592060 0.352397271 0.190747147
## 3025 3027 3028 3029 3030 3031
## 0.497882383 0.019398015 0.122854095 0.682163525 0.788852607 0.937570722
## 3032 3033 3034 3036 3037 3038
## 1.477482138 0.682163525 0.484950373 0.064660050 0.410591316 0.300669232
## 3039 3040 3041 3042 3043 3044
## 0.821182632 1.564773205 0.678930523 1.018395784 0.436455336 1.121851864
## 3046 3047 3048 3049 3051 3052
## 1.917170476 0.475251366 0.161650124 0.168116129 0.459086353 0.181048139
## 3053 3054 3055 3056 3057 3058
## 0.287737222 0.352397271 0.701561540 1.299667001 0.559309431 1.642365265
## 3059 3060 3061 3064 3065 3066
## 0.216611167 0.591639456 0.051728040 0.879376677 0.452620349 0.378261291
## 3067 3068 3070 3071 3072 3073
## 0.423523326 0.956968737 1.218841939 0.908473700 1.416055090 2.499110924
## 3074 3075 3076 3078 3079 3081
## 0.274805212 0.290970224 0.439688339 0.517280398 0.872910672 0.956968737
## 3082 3083 3084 3085 3087 3088
## 0.439688339 0.569008438 1.319065016 0.358863276 0.375028289 0.478484368
## 3089 3090 3093 3094 3095 3096
## 0.080825062 0.006466005 0.064660050 0.161650124 0.336232259 0.006466005
## 3099 3101 3102 3103 3104 3105
## 0.006466005 1.367560053 0.384727296 0.998997769 1.231773948 0.452620349
## 3106 3107 3108 3109 3111 3113
## 0.245708189 0.627202483 0.775920597 0.407358314 0.148718115 0.054961042
## 3115 3116 3121 3122 3123 3124
## 0.009699007 0.029097022 1.823413404 1.231773948 0.843813650 1.199443924
## 3125 3126 3127 3128 3130 3131
## 0.711260548 0.420290324 1.192977919 0.349164269 0.520513401 0.384727296
## 3132 3133 3134 3135 3136 3137
## 0.303902234 0.258640199 0.342698264 0.345931266 0.494649381 0.058194045
## 3138 3140 3141 3142 3143 3144
## 0.158417122 0.012932010 1.283501988 0.837347645 0.695095535 0.717726553
## 3145 3146 3147 3148 3149 3150
## 1.141249879 1.471016133 0.882609680 0.365329281 0.653066503 0.879376677
## 3151 3152 3153 3154 3155 3156
## 0.148718115 0.274805212 0.197213152 0.012932010 0.135786105 0.177815137
## 3158 3160 3161 3162 3163 3165
## 0.003233002 0.009699007 0.252174194 0.417057321 1.513045165 1.732889334
## 3166 3167 3168 3169 3170 3171
## 0.675697520 0.426756329 0.187514144 0.187514144 0.258640199 0.126087097
## 3172 3173 3174 3175 3177 3178
## 0.181048139 0.119621092 0.226310174 0.345931266 0.080825062 0.109922085
## 3179 3180 3181 3182 3183 3184
## 0.045262035 0.045262035 1.393424073 1.118618861 0.177815137 0.853512657
## 3185 3186 3187 3188 3189 3190
## 0.481717371 1.377259060 1.160647894 1.180045909 0.355630274 0.297436229
## 3191 3192 3193 3194 3195 3196
## 0.258640199 0.410591316 0.371795286 0.281271217 0.598105461 0.332999256
## 3197 3198 3199 3200 3201 3202
## 0.087291067 0.119621092 0.494649381 0.048495037 0.067893052 0.006466005
## 3204 3205 3206 3207 3335 3337
## 1.509812163 0.556076428 0.620736478 1.066890822 0.029097022 0.258640199
## 3338 3340 3427 3428 3429 3431
## 0.181048139 0.022631017 0.022631017 0.003233002 0.400892309 0.019398015
## 3437 3438 3750 3752 3754 3756
## 0.087291067 0.019398015 0.155184119 0.303902234 0.297436229 0.042029032
## 3757 3765 3770 3775 3777 3782
## 0.012932010 0.006466005 0.003233002 0.003233002 0.006466005 0.012932010
## 3788 3793 3795 3796 3802 3803
## 0.003233002 0.003233002 0.006466005 0.009699007 0.067893052 0.032330025
## 3805 3806 3807 3808 3809 3810
## 0.064660050 0.132553102 0.022631017 0.006466005 0.012932010 0.051728040
## 3910 3975 3976 3977 3978
## 0.054961042 0.006466005 0.022631017 0.090524070 0.0096990075. Two-way contingency tables for the categorical variables in the dataset.
two-way table for the variables: Type and Method.
table22<-xtabs(~MHousing.df$Type+MHousing.df$Method)
table22## MHousing.df$Method
## MHousing.df$Type PI PN S SA SN SP SS VB W
## h 2733 155 12555 138 944 2896 23 1630 86
## t 533 40 1693 24 108 446 4 319 11
## u 949 69 3439 40 171 1161 4 707 53prop.table(table22)*100## MHousing.df$Method
## MHousing.df$Type PI PN S SA
## h 8.83579580 0.50111539 40.59034625 0.44615434
## t 1.72319033 0.12932010 5.47347321 0.07759206
## u 3.06811936 0.22307717 11.11829556 0.12932010
## MHousing.df$Method
## MHousing.df$Type SN SP SS VB
## h 3.05195435 9.36277521 0.07435906 5.26979406
## t 0.34916427 1.44191911 0.01293201 1.03132779
## u 0.55284343 3.75351589 0.01293201 2.28573276
## MHousing.df$Method
## MHousing.df$Type W
## h 0.27803821
## t 0.03556303
## u 0.17134913two-way table for the variables: Type and SellerG.
table44<-xtabs(~MHousing.df$Type+ MHousing.df$SellerG)
table44## MHousing.df$SellerG
## MHousing.df$Type @Realty A Abercromby's Ace AIME Airport Alex
## h 9 1 31 4 0 1 1
## t 0 0 6 0 1 0 0
## u 0 0 8 0 0 0 0
## MHousing.df$SellerG
## MHousing.df$Type Alexkarbon Allan Allens Anderson Appleby Aquire Area
## h 27 1 1 1 8 4 5
## t 15 0 3 0 0 0 0
## u 25 0 1 8 0 3 1
## MHousing.df$SellerG
## MHousing.df$Type Ascend Ash ASL Asset Assisi Aumeca Australian Avion
## h 3 3 5 5 4 0 1 2
## t 1 0 0 0 1 1 0 0
## u 0 0 0 1 0 0 1 1
## MHousing.df$SellerG
## MHousing.df$Type Barlow Barry Batty Bayside Bekdon Beller Bells Benchmark
## h 36 2238 0 4 16 8 65 1
## t 0 267 1 4 3 4 5 0
## u 16 327 0 0 4 19 9 0
## MHousing.df$SellerG
## MHousing.df$Type Benlor Besser Better Biggin Black Blackbird Blue Bombay
## h 1 0 0 487 1 2 1 1
## t 0 3 0 68 0 0 0 0
## u 1 2 1 263 0 0 0 0
## MHousing.df$SellerG
## MHousing.df$Type Boran Boutique Bowman Brace Brad Bradly Branon Buckingham
## h 1 5 7 7 414 1 4 81
## t 0 0 0 7 67 0 3 14
## u 1 0 0 2 154 0 0 7
## MHousing.df$SellerG
## MHousing.df$Type Bullen Burnham Burns Bustin Buxton Buxton/Advantage
## h 4 40 1 0 971 0
## t 0 3 0 0 353 0
## u 0 16 0 1 320 2
## MHousing.df$SellerG
## MHousing.df$Type Buxton/Buxton Buxton/Find Buxton/Marshall buyMyplace C21
## h 0 0 1 8 104
## t 1 0 1 1 15
## u 0 1 0 1 26
## MHousing.df$SellerG
## MHousing.df$Type Caine Calder Can CarlinSmith Carter Castran CASTRAN
## h 10 12 1 1 23 10 0
## t 3 1 0 0 1 3 0
## u 26 0 0 0 2 17 1
## MHousing.df$SellerG
## MHousing.df$Type Cayzer Century Chambers Changing Charlton Charter
## h 53 1 11 2 15 1
## t 8 1 3 1 4 1
## u 30 1 2 0 6 0
## MHousing.df$SellerG
## MHousing.df$Type Chisholm Christopher Christou Clairmont Collings Collins
## h 68 5 1 0 6 88
## t 9 3 0 1 1 5
## u 76 3 0 0 1 22
## MHousing.df$SellerG
## MHousing.df$Type Community Compton Conquest Considine Cooper Coventry
## h 3 7 1 46 1 1
## t 0 3 0 5 0 0
## u 2 4 0 3 0 0
## MHousing.df$SellerG
## MHousing.df$Type Craig Crane D'Aprano Daniel Darras Darren David Del
## h 4 4 7 7 3 70 2 17
## t 0 0 2 0 0 4 0 0
## u 0 0 0 0 0 21 1 2
## MHousing.df$SellerG
## MHousing.df$Type Dingle Direct Dixon Domain Donovan Douglas Edward Elite
## h 5 0 2 7 1 134 16 2
## t 0 0 0 0 0 8 12 0
## u 46 1 4 9 0 13 27 2
## MHousing.df$SellerG
## MHousing.df$Type Emerson Eric Eview First Flannagan Fletchers
## h 3 3 67 5 9 596
## t 0 0 38 1 0 66
## u 0 0 6 2 3 123
## MHousing.df$SellerG
## MHousing.df$Type Fletchers/Fletchers Fletchers/One FN Follett For
## h 2 1 48 4 1
## t 0 0 2 0 0
## u 1 0 5 0 0
## MHousing.df$SellerG
## MHousing.df$Type Foxtons Frank Free Galldon Gardiner Garvey Gary
## h 0 42 1 0 6 8 153
## t 0 22 0 0 0 0 56
## u 1 8 0 3 0 4 166
## MHousing.df$SellerG
## MHousing.df$Type Gellibrand Geoff GL Grant's Grantham Greg Gunn&Co H
## h 1 0 30 2 2 312 15 1
## t 0 0 1 0 0 31 3 0
## u 0 1 0 0 1 112 4 0
## MHousing.df$SellerG
## MHousing.df$Type Hall Ham Hamilton HAR Harcourts Harcourts/Barry
## h 10 3 1 191 267 0
## t 1 2 2 10 25 0
## u 2 4 1 36 89 1
## MHousing.df$SellerG
## MHousing.df$Type Harrington Haughton Hayeswinckle hockingstuart
## h 21 34 2 1362
## t 2 1 0 236
## u 9 4 0 807
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/Advantage hockingstuart/Barry
## h 0 1
## t 0 0
## u 2 0
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/Buxton hockingstuart/Harcourts
## h 0 1
## t 1 0
## u 0 0
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/hockingstuart hockingstuart/Jellis
## h 5 0
## t 0 0
## u 2 1
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/Sweeney hockingstuart/Village Hodges
## h 1 1 220
## t 0 0 55
## u 0 1 95
## MHousing.df$SellerG
## MHousing.df$Type Holland Home Homes Hooper Hoskins Hunter Icon Iconek
## h 9 0 3 1 10 17 0 29
## t 2 0 0 0 1 2 0 0
## u 0 2 0 1 1 1 1 1
## MHousing.df$SellerG
## MHousing.df$Type iHomes Inner iOne iProperty iSell iTRAK J Jas Jason
## h 1 0 0 0 20 28 25 285 31
## t 0 0 1 1 3 0 2 63 0
## u 0 2 0 0 8 3 6 67 8
## MHousing.df$SellerG
## MHousing.df$Type Jeffrey Jellis Jim JMRE Joe John Johnston Joseph JRW
## h 0 1959 2 3 1 1 1 1 3
## t 0 362 0 1 0 0 1 2 1
## u 1 648 0 2 0 0 0 0 0
## MHousing.df$SellerG
## MHousing.df$Type Just Justin JY K.R.Peters Karen Kay Kaye Keatings
## h 2 1 2 1 1 216 2 3
## t 0 0 0 0 0 25 0 0
## u 0 1 0 0 0 83 0 0
## MHousing.df$SellerG
## MHousing.df$Type Kelly Ken Knight L Landfield Langwell Le Leaders
## h 4 3 1 9 0 2 4 1
## t 0 0 0 0 1 0 1 0
## u 2 0 0 0 0 2 2 1
## MHousing.df$SellerG
## MHousing.df$Type Leading Leased Leeburn Leyton Lindellas LITTLE LJ LJH
## h 8 4 12 3 21 11 77 4
## t 0 0 1 0 3 6 4 0
## u 0 0 0 1 8 50 3 0
## MHousing.df$SellerG
## MHousing.df$Type LLC Love Lucas Luxe Luxton M.J Maddison Maitland
## h 8 154 2 0 1 2 4 3
## t 2 20 1 0 1 0 1 0
## u 0 60 4 1 0 1 0 2
## MHousing.df$SellerG
## MHousing.df$Type Malvern Mandy Mark Marshall Marvelli Mason Matthew Max
## h 1 3 1 1452 1 11 2 10
## t 0 1 0 98 0 0 1 0
## u 0 1 0 285 0 1 2 4
## MHousing.df$SellerG
## MHousing.df$Type McDonald McEwing McGrath McGrath/Langwell McLennan
## h 53 1 292 1 12
## t 8 0 65 0 0
## u 14 0 153 0 3
## MHousing.df$SellerG
## MHousing.df$Type McNaughton Meadows Meallin Melbourne Metro MICM Miles
## h 3 1 1 19 2 6 299
## t 0 0 0 5 0 0 40
## u 0 1 0 2 1 39 105
## MHousing.df$SellerG
## MHousing.df$Type Millership Mindacom Mitchell MJ Moonee Morleys Morrison
## h 24 1 0 8 58 1 80
## t 0 0 0 0 5 3 6
## u 2 1 1 0 8 11 10
## MHousing.df$SellerG
## MHousing.df$Type MSM Munn Naison Nardella Nelson New Nguyen Nicholas
## h 0 2 1 2 2123 5 2 1
## t 0 0 0 1 312 1 4 0
## u 2 0 0 1 499 0 1 0
## MHousing.df$SellerG
## MHousing.df$Type Nicholls Nicholson Nick Noel North O'Brien O'Donoghues
## h 3 10 77 295 1 142 5
## t 0 1 7 53 0 13 3
## u 1 3 14 107 0 24 3
## MHousing.df$SellerG
## MHousing.df$Type Oak Obrien OBrien One Only Open Oriental Owen P
## h 0 10 4 16 2 0 3 2 1
## t 0 4 0 0 0 0 0 0 0
## u 1 3 0 2 0 1 1 2 0
## MHousing.df$SellerG
## MHousing.df$Type Pagan Parkes Parkinson Paul Pavilion Peake Peninsula
## h 5 17 2 14 1 4 0
## t 3 2 1 0 0 0 0
## u 45 5 0 5 0 1 1
## MHousing.df$SellerG
## MHousing.df$Type Peter Philip Point PRD PRDNationwide Pride Prime
## h 36 107 9 2 20 6 1
## t 6 15 0 0 0 2 0
## u 17 21 1 0 0 8 0
## MHousing.df$SellerG
## MHousing.df$Type Private/Tiernan's Prof. Professionals Property Propertyau
## h 1 39 2 3 1
## t 0 2 0 2 0
## u 0 3 1 1 0
## MHousing.df$SellerG
## MHousing.df$Type Prowse Purplebricks Quinta R&H Raine Raine&Horne Ray
## h 0 95 1 4 236 5 1225
## t 0 17 0 0 18 0 197
## u 2 37 0 5 25 0 273
## MHousing.df$SellerG
## MHousing.df$Type Rayner Re RE Reach Real Red Redina Reed Reliance
## h 2 11 12 0 2 6 0 0 39
## t 0 0 2 1 1 0 2 1 2
## u 0 1 0 1 0 1 1 0 0
## MHousing.df$SellerG
## MHousing.df$Type REMAX Rendina Rexhepi Ristic Rodney Roger Rombotis Rosin
## h 7 72 2 5 6 1 0 0
## t 0 17 0 0 7 0 1 0
## u 0 25 0 0 15 0 2 1
## MHousing.df$SellerG
## MHousing.df$Type Ross Rounds Royston RT RW Ryder S&L Sanctuary
## h 6 4 0 318 182 12 27 1
## t 1 0 0 36 28 0 0 0
## u 6 3 1 126 25 0 3 0
## MHousing.df$SellerG
## MHousing.df$Type Sandhurst Schroeder Scott Sell Shape Silver Smart SN
## h 1 8 4 5 1 1 2 1
## t 0 0 1 1 0 0 0 0
## u 0 4 1 1 5 0 2 0
## MHousing.df$SellerG
## MHousing.df$Type Sotheby's Space Spencer Steller Sterling Steveway
## h 7 0 0 0 1 1
## t 1 0 0 0 0 0
## u 9 2 1 8 0 0
## MHousing.df$SellerG
## MHousing.df$Type Stockdale Surreal Sutherland Sweeney Sweeney/Advantage
## h 282 2 0 285 2
## t 39 0 0 37 0
## u 39 0 1 63 0
## MHousing.df$SellerG
## MHousing.df$Type Sweeney/Burnham T Tanner The Thomas Thomson Tiernan's
## h 0 1 0 14 7 35 1
## t 0 0 0 0 0 7 0
## u 1 0 1 4 1 54 0
## MHousing.df$SellerG
## MHousing.df$Type Tim Trimson Triwest TRUE U Upper Upside Veitch Vic
## h 20 5 13 4 4 1 2 1 1
## t 3 2 0 0 0 0 1 0 0
## u 4 9 2 0 0 0 1 0 0
## MHousing.df$SellerG
## MHousing.df$Type VicHomes Vicprop VICProp VICPROP Victory Village W.B.
## h 1 0 3 4 1 165 6
## t 0 0 1 0 0 15 2
## u 0 1 0 2 0 20 8
## MHousing.df$SellerG
## MHousing.df$Type Walsh Walshe Waterfront Weast Weda WeSell Weston Westside
## h 2 21 1 1 3 1 2 12
## t 1 4 0 0 0 0 0 0
## u 2 27 0 0 4 0 1 0
## MHousing.df$SellerG
## MHousing.df$Type White WHITEFOX Whiting William Williams Wilson Win Wood
## h 2 6 11 21 109 9 20 0
## t 0 0 4 3 12 0 3 0
## u 0 0 18 2 59 28 1 2
## MHousing.df$SellerG
## MHousing.df$Type Woodards Wyndham Xynergy YPA Zahn
## h 407 1 1 333 1
## t 39 0 1 16 0
## u 198 0 1 24 0prop.table(table44)*100## MHousing.df$SellerG
## MHousing.df$Type @Realty A Abercromby's Ace
## h 0.029097022 0.003233002 0.100223077 0.012932010
## t 0.000000000 0.000000000 0.019398015 0.000000000
## u 0.000000000 0.000000000 0.025864020 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type AIME Airport Alex Alexkarbon
## h 0.000000000 0.003233002 0.003233002 0.087291067
## t 0.003233002 0.000000000 0.000000000 0.048495037
## u 0.000000000 0.000000000 0.000000000 0.080825062
## MHousing.df$SellerG
## MHousing.df$Type Allan Allens Anderson Appleby
## h 0.003233002 0.003233002 0.003233002 0.025864020
## t 0.000000000 0.009699007 0.000000000 0.000000000
## u 0.000000000 0.003233002 0.025864020 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Aquire Area Ascend Ash
## h 0.012932010 0.016165012 0.009699007 0.009699007
## t 0.000000000 0.000000000 0.003233002 0.000000000
## u 0.009699007 0.003233002 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type ASL Asset Assisi Aumeca
## h 0.016165012 0.016165012 0.012932010 0.000000000
## t 0.000000000 0.000000000 0.003233002 0.003233002
## u 0.000000000 0.003233002 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Australian Avion Barlow Barry
## h 0.003233002 0.006466005 0.116388090 7.235459571
## t 0.000000000 0.000000000 0.000000000 0.863211665
## u 0.003233002 0.003233002 0.051728040 1.057191814
## MHousing.df$SellerG
## MHousing.df$Type Batty Bayside Bekdon Beller
## h 0.000000000 0.012932010 0.051728040 0.025864020
## t 0.003233002 0.012932010 0.009699007 0.012932010
## u 0.000000000 0.000000000 0.012932010 0.061427047
## MHousing.df$SellerG
## MHousing.df$Type Bells Benchmark Benlor Besser
## h 0.210145162 0.003233002 0.003233002 0.000000000
## t 0.016165012 0.000000000 0.000000000 0.009699007
## u 0.029097022 0.000000000 0.003233002 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Better Biggin Black Blackbird
## h 0.000000000 1.574472212 0.003233002 0.006466005
## t 0.000000000 0.219844169 0.000000000 0.000000000
## u 0.003233002 0.850279655 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Blue Bombay Boran Boutique
## h 0.003233002 0.003233002 0.003233002 0.016165012
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.000000000 0.000000000 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Bowman Brace Brad Bradly
## h 0.022631017 0.022631017 1.338463031 0.003233002
## t 0.000000000 0.022631017 0.216611167 0.000000000
## u 0.000000000 0.006466005 0.497882383 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Branon Buckingham Bullen Burnham
## h 0.012932010 0.261873202 0.012932010 0.129320100
## t 0.009699007 0.045262035 0.000000000 0.009699007
## u 0.000000000 0.022631017 0.000000000 0.051728040
## MHousing.df$SellerG
## MHousing.df$Type Burns Bustin Buxton Buxton/Advantage
## h 0.003233002 0.000000000 3.139245417 0.000000000
## t 0.000000000 0.000000000 1.141249879 0.000000000
## u 0.000000000 0.003233002 1.034560797 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Buxton/Buxton Buxton/Find Buxton/Marshall buyMyplace
## h 0.000000000 0.000000000 0.003233002 0.025864020
## t 0.003233002 0.000000000 0.003233002 0.003233002
## u 0.000000000 0.003233002 0.000000000 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type C21 Caine Calder Can
## h 0.336232259 0.032330025 0.038796030 0.003233002
## t 0.048495037 0.009699007 0.003233002 0.000000000
## u 0.084058065 0.084058065 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type CarlinSmith Carter Castran CASTRAN
## h 0.003233002 0.074359057 0.032330025 0.000000000
## t 0.000000000 0.003233002 0.009699007 0.000000000
## u 0.000000000 0.006466005 0.054961042 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Cayzer Century Chambers Changing
## h 0.171349132 0.003233002 0.035563027 0.006466005
## t 0.025864020 0.003233002 0.009699007 0.003233002
## u 0.096990075 0.003233002 0.006466005 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Charlton Charter Chisholm Christopher
## h 0.048495037 0.003233002 0.219844169 0.016165012
## t 0.012932010 0.003233002 0.029097022 0.009699007
## u 0.019398015 0.000000000 0.245708189 0.009699007
## MHousing.df$SellerG
## MHousing.df$Type Christou Clairmont Collings Collins
## h 0.003233002 0.000000000 0.019398015 0.284504219
## t 0.000000000 0.003233002 0.003233002 0.016165012
## u 0.000000000 0.000000000 0.003233002 0.071126055
## MHousing.df$SellerG
## MHousing.df$Type Community Compton Conquest Considine
## h 0.009699007 0.022631017 0.003233002 0.148718115
## t 0.000000000 0.009699007 0.000000000 0.016165012
## u 0.006466005 0.012932010 0.000000000 0.009699007
## MHousing.df$SellerG
## MHousing.df$Type Cooper Coventry Craig Crane
## h 0.003233002 0.003233002 0.012932010 0.012932010
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.000000000 0.000000000 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type D'Aprano Daniel Darras Darren
## h 0.022631017 0.022631017 0.009699007 0.226310174
## t 0.006466005 0.000000000 0.000000000 0.012932010
## u 0.000000000 0.000000000 0.000000000 0.067893052
## MHousing.df$SellerG
## MHousing.df$Type David Del Dingle Direct
## h 0.006466005 0.054961042 0.016165012 0.000000000
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.003233002 0.006466005 0.148718115 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Dixon Domain Donovan Douglas
## h 0.006466005 0.022631017 0.003233002 0.433222334
## t 0.000000000 0.000000000 0.000000000 0.025864020
## u 0.012932010 0.029097022 0.000000000 0.042029032
## MHousing.df$SellerG
## MHousing.df$Type Edward Elite Emerson Eric
## h 0.051728040 0.006466005 0.009699007 0.009699007
## t 0.038796030 0.000000000 0.000000000 0.000000000
## u 0.087291067 0.006466005 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Eview First Flannagan Fletchers
## h 0.216611167 0.016165012 0.029097022 1.926869484
## t 0.122854095 0.003233002 0.000000000 0.213378164
## u 0.019398015 0.006466005 0.009699007 0.397659306
## MHousing.df$SellerG
## MHousing.df$Type Fletchers/Fletchers Fletchers/One FN Follett
## h 0.006466005 0.003233002 0.155184119 0.012932010
## t 0.000000000 0.000000000 0.006466005 0.000000000
## u 0.003233002 0.000000000 0.016165012 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type For Foxtons Frank Free
## h 0.003233002 0.000000000 0.135786105 0.003233002
## t 0.000000000 0.000000000 0.071126055 0.000000000
## u 0.000000000 0.003233002 0.025864020 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Galldon Gardiner Garvey Gary
## h 0.000000000 0.019398015 0.025864020 0.494649381
## t 0.000000000 0.000000000 0.000000000 0.181048139
## u 0.009699007 0.000000000 0.012932010 0.536678413
## MHousing.df$SellerG
## MHousing.df$Type Gellibrand Geoff GL Grant's
## h 0.003233002 0.000000000 0.096990075 0.006466005
## t 0.000000000 0.000000000 0.003233002 0.000000000
## u 0.000000000 0.003233002 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Grantham Greg Gunn&Co H
## h 0.006466005 1.008696777 0.048495037 0.003233002
## t 0.000000000 0.100223077 0.009699007 0.000000000
## u 0.003233002 0.362096279 0.012932010 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Hall Ham Hamilton HAR
## h 0.032330025 0.009699007 0.003233002 0.617503475
## t 0.003233002 0.006466005 0.006466005 0.032330025
## u 0.006466005 0.012932010 0.003233002 0.116388090
## MHousing.df$SellerG
## MHousing.df$Type Harcourts Harcourts/Barry Harrington Haughton
## h 0.863211665 0.000000000 0.067893052 0.109922085
## t 0.080825062 0.000000000 0.006466005 0.003233002
## u 0.287737222 0.003233002 0.029097022 0.012932010
## MHousing.df$SellerG
## MHousing.df$Type Hayeswinckle hockingstuart hockingstuart/Advantage
## h 0.006466005 4.403349391 0.000000000
## t 0.000000000 0.762988588 0.000000000
## u 0.000000000 2.609033009 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/Barry hockingstuart/Buxton
## h 0.003233002 0.000000000
## t 0.000000000 0.003233002
## u 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/Harcourts hockingstuart/hockingstuart
## h 0.003233002 0.016165012
## t 0.000000000 0.000000000
## u 0.000000000 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/Jellis hockingstuart/Sweeney
## h 0.000000000 0.003233002
## t 0.000000000 0.000000000
## u 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type hockingstuart/Village Hodges Holland Home
## h 0.003233002 0.711260548 0.029097022 0.000000000
## t 0.000000000 0.177815137 0.006466005 0.000000000
## u 0.003233002 0.307135236 0.000000000 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Homes Hooper Hoskins Hunter
## h 0.009699007 0.003233002 0.032330025 0.054961042
## t 0.000000000 0.000000000 0.003233002 0.006466005
## u 0.000000000 0.003233002 0.003233002 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Icon Iconek iHomes Inner
## h 0.000000000 0.093757072 0.003233002 0.000000000
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.003233002 0.003233002 0.000000000 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type iOne iProperty iSell iTRAK
## h 0.000000000 0.000000000 0.064660050 0.090524070
## t 0.003233002 0.003233002 0.009699007 0.000000000
## u 0.000000000 0.000000000 0.025864020 0.009699007
## MHousing.df$SellerG
## MHousing.df$Type J Jas Jason Jeffrey
## h 0.080825062 0.921405709 0.100223077 0.000000000
## t 0.006466005 0.203679157 0.000000000 0.000000000
## u 0.019398015 0.216611167 0.025864020 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Jellis Jim JMRE Joe
## h 6.333451877 0.006466005 0.009699007 0.003233002
## t 1.170346901 0.000000000 0.003233002 0.000000000
## u 2.094985613 0.000000000 0.006466005 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type John Johnston Joseph JRW
## h 0.003233002 0.003233002 0.003233002 0.009699007
## t 0.000000000 0.003233002 0.006466005 0.003233002
## u 0.000000000 0.000000000 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Just Justin JY K.R.Peters
## h 0.006466005 0.003233002 0.006466005 0.003233002
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.000000000 0.003233002 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Karen Kay Kaye Keatings
## h 0.003233002 0.698328538 0.006466005 0.009699007
## t 0.000000000 0.080825062 0.000000000 0.000000000
## u 0.000000000 0.268339207 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Kelly Ken Knight L
## h 0.012932010 0.009699007 0.003233002 0.029097022
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.006466005 0.000000000 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Landfield Langwell Le Leaders
## h 0.000000000 0.006466005 0.012932010 0.003233002
## t 0.003233002 0.000000000 0.003233002 0.000000000
## u 0.000000000 0.006466005 0.006466005 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Leading Leased Leeburn Leyton
## h 0.025864020 0.012932010 0.038796030 0.009699007
## t 0.000000000 0.000000000 0.003233002 0.000000000
## u 0.000000000 0.000000000 0.000000000 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Lindellas LITTLE LJ LJH
## h 0.067893052 0.035563027 0.248941192 0.012932010
## t 0.009699007 0.019398015 0.012932010 0.000000000
## u 0.025864020 0.161650124 0.009699007 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type LLC Love Lucas Luxe
## h 0.025864020 0.497882383 0.006466005 0.000000000
## t 0.006466005 0.064660050 0.003233002 0.000000000
## u 0.000000000 0.193980149 0.012932010 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Luxton M.J Maddison Maitland
## h 0.003233002 0.006466005 0.012932010 0.009699007
## t 0.003233002 0.000000000 0.003233002 0.000000000
## u 0.000000000 0.003233002 0.000000000 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Malvern Mandy Mark Marshall
## h 0.003233002 0.009699007 0.003233002 4.694319615
## t 0.000000000 0.003233002 0.000000000 0.316834244
## u 0.000000000 0.003233002 0.000000000 0.921405709
## MHousing.df$SellerG
## MHousing.df$Type Marvelli Mason Matthew Max
## h 0.003233002 0.035563027 0.006466005 0.032330025
## t 0.000000000 0.000000000 0.003233002 0.000000000
## u 0.000000000 0.003233002 0.006466005 0.012932010
## MHousing.df$SellerG
## MHousing.df$Type McDonald McEwing McGrath McGrath/Langwell
## h 0.171349132 0.003233002 0.944036727 0.003233002
## t 0.025864020 0.000000000 0.210145162 0.000000000
## u 0.045262035 0.000000000 0.494649381 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type McLennan McNaughton Meadows Meallin
## h 0.038796030 0.009699007 0.003233002 0.003233002
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.009699007 0.000000000 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Melbourne Metro MICM Miles
## h 0.061427047 0.006466005 0.019398015 0.966667744
## t 0.016165012 0.000000000 0.000000000 0.129320100
## u 0.006466005 0.003233002 0.126087097 0.339465261
## MHousing.df$SellerG
## MHousing.df$Type Millership Mindacom Mitchell MJ
## h 0.077592060 0.003233002 0.000000000 0.025864020
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.006466005 0.003233002 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Moonee Morleys Morrison MSM
## h 0.187514144 0.003233002 0.258640199 0.000000000
## t 0.016165012 0.009699007 0.019398015 0.000000000
## u 0.025864020 0.035563027 0.032330025 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Munn Naison Nardella Nelson
## h 0.006466005 0.003233002 0.006466005 6.863664285
## t 0.000000000 0.000000000 0.003233002 1.008696777
## u 0.000000000 0.000000000 0.003233002 1.613268242
## MHousing.df$SellerG
## MHousing.df$Type New Nguyen Nicholas Nicholls
## h 0.016165012 0.006466005 0.003233002 0.009699007
## t 0.003233002 0.012932010 0.000000000 0.000000000
## u 0.000000000 0.003233002 0.000000000 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Nicholson Nick Noel North
## h 0.032330025 0.248941192 0.953735734 0.003233002
## t 0.003233002 0.022631017 0.171349132 0.000000000
## u 0.009699007 0.045262035 0.345931266 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type O'Brien O'Donoghues Oak Obrien
## h 0.459086353 0.016165012 0.000000000 0.032330025
## t 0.042029032 0.009699007 0.000000000 0.012932010
## u 0.077592060 0.009699007 0.003233002 0.009699007
## MHousing.df$SellerG
## MHousing.df$Type OBrien One Only Open
## h 0.012932010 0.051728040 0.006466005 0.000000000
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.000000000 0.006466005 0.000000000 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Oriental Owen P Pagan
## h 0.009699007 0.006466005 0.003233002 0.016165012
## t 0.000000000 0.000000000 0.000000000 0.009699007
## u 0.003233002 0.006466005 0.000000000 0.145485112
## MHousing.df$SellerG
## MHousing.df$Type Parkes Parkinson Paul Pavilion
## h 0.054961042 0.006466005 0.045262035 0.003233002
## t 0.006466005 0.003233002 0.000000000 0.000000000
## u 0.016165012 0.000000000 0.016165012 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Peake Peninsula Peter Philip
## h 0.012932010 0.000000000 0.116388090 0.345931266
## t 0.000000000 0.000000000 0.019398015 0.048495037
## u 0.003233002 0.003233002 0.054961042 0.067893052
## MHousing.df$SellerG
## MHousing.df$Type Point PRD PRDNationwide Pride
## h 0.029097022 0.006466005 0.064660050 0.019398015
## t 0.000000000 0.000000000 0.000000000 0.006466005
## u 0.003233002 0.000000000 0.000000000 0.025864020
## MHousing.df$SellerG
## MHousing.df$Type Prime Private/Tiernan's Prof. Professionals
## h 0.003233002 0.003233002 0.126087097 0.006466005
## t 0.000000000 0.000000000 0.006466005 0.000000000
## u 0.000000000 0.000000000 0.009699007 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Property Propertyau Prowse Purplebricks
## h 0.009699007 0.003233002 0.000000000 0.307135236
## t 0.006466005 0.000000000 0.000000000 0.054961042
## u 0.003233002 0.000000000 0.006466005 0.119621092
## MHousing.df$SellerG
## MHousing.df$Type Quinta R&H Raine Raine&Horne
## h 0.003233002 0.012932010 0.762988588 0.016165012
## t 0.000000000 0.000000000 0.058194045 0.000000000
## u 0.000000000 0.016165012 0.080825062 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Ray Rayner Re RE
## h 3.960428050 0.006466005 0.035563027 0.038796030
## t 0.636901490 0.000000000 0.000000000 0.006466005
## u 0.882609680 0.000000000 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Reach Real Red Redina
## h 0.000000000 0.006466005 0.019398015 0.000000000
## t 0.003233002 0.003233002 0.000000000 0.006466005
## u 0.003233002 0.000000000 0.003233002 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type Reed Reliance REMAX Rendina
## h 0.000000000 0.126087097 0.022631017 0.232776179
## t 0.003233002 0.006466005 0.000000000 0.054961042
## u 0.000000000 0.000000000 0.000000000 0.080825062
## MHousing.df$SellerG
## MHousing.df$Type Rexhepi Ristic Rodney Roger
## h 0.006466005 0.016165012 0.019398015 0.003233002
## t 0.000000000 0.000000000 0.022631017 0.000000000
## u 0.000000000 0.000000000 0.048495037 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Rombotis Rosin Ross Rounds
## h 0.000000000 0.000000000 0.019398015 0.012932010
## t 0.003233002 0.000000000 0.003233002 0.000000000
## u 0.006466005 0.003233002 0.019398015 0.009699007
## MHousing.df$SellerG
## MHousing.df$Type Royston RT RW Ryder
## h 0.000000000 1.028094792 0.588406453 0.038796030
## t 0.000000000 0.116388090 0.090524070 0.000000000
## u 0.003233002 0.407358314 0.080825062 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type S&L Sanctuary Sandhurst Schroeder
## h 0.087291067 0.003233002 0.003233002 0.025864020
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.009699007 0.000000000 0.000000000 0.012932010
## MHousing.df$SellerG
## MHousing.df$Type Scott Sell Shape Silver
## h 0.012932010 0.016165012 0.003233002 0.003233002
## t 0.003233002 0.003233002 0.000000000 0.000000000
## u 0.003233002 0.003233002 0.016165012 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Smart SN Sotheby's Space
## h 0.006466005 0.003233002 0.022631017 0.000000000
## t 0.000000000 0.000000000 0.003233002 0.000000000
## u 0.006466005 0.000000000 0.029097022 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Spencer Steller Sterling Steveway
## h 0.000000000 0.000000000 0.003233002 0.003233002
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.003233002 0.025864020 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Stockdale Surreal Sutherland Sweeney
## h 0.911706702 0.006466005 0.000000000 0.921405709
## t 0.126087097 0.000000000 0.000000000 0.119621092
## u 0.126087097 0.000000000 0.003233002 0.203679157
## MHousing.df$SellerG
## MHousing.df$Type Sweeney/Advantage Sweeney/Burnham T Tanner
## h 0.006466005 0.000000000 0.003233002 0.000000000
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.000000000 0.003233002 0.000000000 0.003233002
## MHousing.df$SellerG
## MHousing.df$Type The Thomas Thomson Tiernan's
## h 0.045262035 0.022631017 0.113155087 0.003233002
## t 0.000000000 0.000000000 0.022631017 0.000000000
## u 0.012932010 0.003233002 0.174582134 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Tim Trimson Triwest TRUE
## h 0.064660050 0.016165012 0.042029032 0.012932010
## t 0.009699007 0.006466005 0.000000000 0.000000000
## u 0.012932010 0.029097022 0.006466005 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type U Upper Upside Veitch
## h 0.012932010 0.003233002 0.006466005 0.003233002
## t 0.000000000 0.000000000 0.003233002 0.000000000
## u 0.000000000 0.000000000 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Vic VicHomes Vicprop VICProp
## h 0.003233002 0.003233002 0.000000000 0.009699007
## t 0.000000000 0.000000000 0.000000000 0.003233002
## u 0.000000000 0.000000000 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type VICPROP Victory Village W.B.
## h 0.012932010 0.003233002 0.533445411 0.019398015
## t 0.000000000 0.000000000 0.048495037 0.006466005
## u 0.006466005 0.000000000 0.064660050 0.025864020
## MHousing.df$SellerG
## MHousing.df$Type Walsh Walshe Waterfront Weast
## h 0.006466005 0.067893052 0.003233002 0.003233002
## t 0.003233002 0.012932010 0.000000000 0.000000000
## u 0.006466005 0.087291067 0.000000000 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type Weda WeSell Weston Westside
## h 0.009699007 0.003233002 0.006466005 0.038796030
## t 0.000000000 0.000000000 0.000000000 0.000000000
## u 0.012932010 0.000000000 0.003233002 0.000000000
## MHousing.df$SellerG
## MHousing.df$Type White WHITEFOX Whiting William
## h 0.006466005 0.019398015 0.035563027 0.067893052
## t 0.000000000 0.000000000 0.012932010 0.009699007
## u 0.000000000 0.000000000 0.058194045 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Williams Wilson Win Wood
## h 0.352397271 0.029097022 0.064660050 0.000000000
## t 0.038796030 0.000000000 0.009699007 0.000000000
## u 0.190747147 0.090524070 0.003233002 0.006466005
## MHousing.df$SellerG
## MHousing.df$Type Woodards Wyndham Xynergy YPA
## h 1.315832013 0.003233002 0.003233002 1.076589829
## t 0.126087097 0.000000000 0.003233002 0.051728040
## u 0.640134493 0.000000000 0.003233002 0.077592060
## MHousing.df$SellerG
## MHousing.df$Type Zahn
## h 0.003233002
## t 0.000000000
## u 0.0000000006. Boxplot of the concerned variables.
boxplot for price.
boxplot(MHousing.df$Price, xlab="Price of the property",ylab="Melbourne Housing", horizontal=TRUE)
boxplot for the number of rooms.
boxplot(MHousing.df$Rooms, xlab="Number of rooms",ylab="Melbourne Housing", horizontal=TRUE)
boxplot for the no. of bedrooms.
boxplot(MHousing.df$Bedroom2, xlab="Number of bedrooms",ylab="Melbourne Housing", horizontal=TRUE)
boxplot for the number of bathrooms.
boxplot(MHousing.df$Bathroom, xlab="Number of bathrooms",ylab="Melbourne Housing", horizontal=TRUE)
boxplot for the number of cars.
boxplot(MHousing.df$Car, xlab="Number of cars",ylab="Melbourne Housing", horizontal=TRUE)
boxplot for the size of the land.
boxplot(MHousing.df$Landsize, xlab="Landsize of the property",ylab="Melbourne Housing", horizontal=TRUE)
boxplot for the building area.
boxplot(MHousing.df$BuildingArea, xlab="Building area of the property",ylab="Melbourne Housing", horizontal=TRUE)
7. Null Hypothesis: There is no significant difference in the prices of the housing which are having more landsize than those which occupy less area.
t.test(MHousing.df$Price, MHousing.df$Landsize)##
## Welch Two Sample t-test
##
## data: MHousing.df$Price and MHousing.df$Landsize
## t = 253.9, df = 24198, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1043882 1060125
## sample estimates:
## mean of x mean of y
## 1052590.4437 587.09888. Corrgram of all the variables.
library("corrgram", lib.loc="~/R/win-library/3.4")
corrgram(MHousing.df, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of Variables")
9. Correlation between variables.
cor.test(MHousing.df$Price, MHousing.df$Rooms)##
## Pearson's product-moment correlation
##
## data: MHousing.df$Price and MHousing.df$Rooms
## t = 82.907, df = 24195, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4604913 0.4801167
## sample estimates:
## cor
## 0.4703622cor.test(MHousing.df$Price, MHousing.df$Landsize)##
## Pearson's product-moment correlation
##
## data: MHousing.df$Price and MHousing.df$Landsize
## t = 4.2764, df = 15944, p-value = 1.91e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01833626 0.04934331
## sample estimates:
## cor
## 0.0338479310. Scatterplot of price and number of rooms.
library("car", lib.loc="~/R/win-library/3.4")##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(MHousing.df$Price,MHousing.df$Rooms,xlab = "price", ylab = "number of rooms")
11. Scatterplot of price and landsize.
library("car", lib.loc="~/R/win-library/3.4")
scatterplot(MHousing.df$Price,MHousing.df$Landsize,xlab = "price", ylab = "landsize")
12. Regression analysis:
regg<- lm(MHousing.df$Price~MHousing.df$Rooms+ MHousing.df$Landsize+MHousing.df$Bedroom2+MHousing.df$Bathroom+MHousing.df$Car)
regg##
## Call:
## lm(formula = MHousing.df$Price ~ MHousing.df$Rooms + MHousing.df$Landsize +
## MHousing.df$Bedroom2 + MHousing.df$Bathroom + MHousing.df$Car)
##
## Coefficients:
## (Intercept) MHousing.df$Rooms MHousing.df$Landsize
## 68734.81 220485.59 2.22
## MHousing.df$Bedroom2 MHousing.df$Bathroom MHousing.df$Car
## -14473.98 243346.87 8416.96summary(regg)##
## Call:
## lm(formula = MHousing.df$Price ~ MHousing.df$Rooms + MHousing.df$Landsize +
## MHousing.df$Bedroom2 + MHousing.df$Bathroom + MHousing.df$Car)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3074991 -323854 -106127 229091 9557236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 68734.805 15027.355 4.574 4.82e-06 ***
## MHousing.df$Rooms 220485.592 14835.579 14.862 < 2e-16 ***
## MHousing.df$Landsize 2.220 1.121 1.980 0.0478 *
## MHousing.df$Bedroom2 -14473.980 14602.386 -0.991 0.3216
## MHousing.df$Bathroom 243346.872 8000.809 30.415 < 2e-16 ***
## MHousing.df$Car 8416.958 4932.561 1.706 0.0880 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 560000 on 15760 degrees of freedom
## (15165 observations deleted due to missingness)
## Multiple R-squared: 0.2621, Adjusted R-squared: 0.2619
## F-statistic: 1120 on 5 and 15760 DF, p-value: < 2.2e-16Summary:
The regression analysis makes it very clear that all the variables are highly dependent on the pricing of the property due to the fact that the value of p<0.05. The t-test tell us that there is a significant difference in the prices of the housing which are having more landsize than those which occupy less area due to the extremely small p-value which makes us reject the null hypothesis.