QV figures show Auckland now just short of $1 million average house price; national average ticks over $600k

By David Hargreaves

The rocket ship that is the New Zealand housing market is showing no sign of losing impetus, with the average national price rushing past $600,000 for the first time, according to the latest monthly QV figures.

Auckland continued its rapid ascent, with its average price rising by some $552 dollars a day during July and ending the month quickly closing on the $1 million mark.

As of July the average in the country’s largest city stood at $992,207, up from $975,087 in June.

The Auckland market has increased 16.0% year on year and 5.2% over the past three months. Values there are now 81.6% higher than the previous peak of 2007.  When adjusted for inflation values rose 15.5% over the past year and are 54.2% above the 2007 peak.

The latest monthly QV House Price Index shows that nationwide residential property values for July have increased 14.1% over the past year. Values rose by 6.1% over the past three months and are now 45.4% above the previous market peak of late 2007. When adjusted for inflation the nationwide annual increase drops slightly to 13.7% and values are now 23.5% above the 2007 peak. The average value nationwide has now ticked over the $600,000s and is $602,434.

In June the national average was $590,909, so nationally house prices rose nearly $372 dollars a day during July – though of course that figure does includes Auckland’s prices.

QV National Spokesperson Andrea Rush said values continued to rise rapidly in many parts of New Zealand – buoyed by low interest rates, strong investor activity and high net migration.

“Hamilton, Queenstown Lakes and Tauranga have seen some of the highest growth with Hamilton values rising 31.5% since July 2015, nearly twice as fast as Auckland which rose 16% over the same period.

“The Auckland market, however, has continued to accelerate – up more than 5% over the past three months.”

Rush said the Wellington and Dunedin residential property markets also continued to show strong growth.

“Christchurch by comparison is showing much more moderate value growth and along with supply meeting demand for homes in the city, there’s now an over-supply of rental properties which is resulting in a downward trend in rents there.”

It was too soon to tell what impact the Reserve Bank’s new 40% deposit requirement for anyone purchasing a property they do not intend to live in, would have on the market, Rush said.

“However there are reports the new rules have already led to some offers being withdrawn by investors in parts of the country.”

See the table below for the average dwelling values throughout the country.

QV House Price Index July 2016:

Territorial authority Average $
current value
12 month change% 3 month change %
Auckland Areas 992,207 16.0% 5.2%
Wellington Areas 524,063 14.4% 5.5%
Main Urban Areas 722,243 14.9% 6.6%
————————————— ———— ———– ———-
Total NZ $ 602,434 14.1% 6.1%
       
Far North 355,485 13.6% 3.6%
Whangarei 421,750 19.9% 6.0%
Kaipara 422,295 15.1% 5.1%
Auckland – Rodney 859,303 16.0% 2.8%
Rodney – Hibiscus Coast 844,826 15.0% 2.2%
Rodney – North 874,992 16.9% 3.4%
Auckland – North Shore 1,160,094 15.1% 5.3%
North Shore – Coastal 1,322,798 15.2% 5.5%
North Shore – Onewa 937,297 13.8% 6.1%
North Shore – North Harbour 1,126,152 16.8% 3.9%
Auckland – Waitakere 782,039 13.8% 3.7%
Auckland – City 1,163,464 14.6% 5.6%
Auckland City – Central 1,003,978 14.9% 4.8%
Auckland_City – East 1,453,988 14.8% 5.5%
Auckland City – South 1,060,567 14.0% 6.4%
Auckland City – Islands 982,998 18.1% 5.6%
Auckland – Manukau 859,005 20.0% 5.7%
Manukau – East 1,100,754 18.2% 5.7%
Manukau – Central 662,182 19.4% 4.5%
Manukau – North West 741,947 23.6% 6.5%
Auckland – Papakura 638,678 21.1% 4.9%
Auckland – Franklin 613,696 15.1% 3.3%
       
Thames Coromandel 586,608 11.5% 3.8%
Hauraki 307,560 18.3% 6.0%
Waikato 389,220 27.5% 4.2%
Matamata Piako 354,534 24.3% 7.3%
Hamilton 513,094 31.5% 8.9%
Hamilton – North East 659,231 33.1% 10.5%
Hamilton – Central & North West 472,328 30.0% 9.2%
Hamilton – South East 463,194 29.6% 6.5%
Hamilton – South West 449,546 31.0% 7.4%
Waipa 445,080 23.2% 4.7%
Otorohanga 250,202 29.0% 12.4%
South Waikato 159,854 24.1% 5.1%
Waitomo 164,555 4.8% 7.1%
Taupo 393,296 13.1% 3.5%
Western BOP 540,128 24.3% 5.9%
Tauranga 615,625 25.7% 6.6%
Rotorua 338,679 24.8% 9.2%
Whakatane 356,353 17.6% 8.1%
Kawerau 143,765 41.0% 15.5%
Opotiki 238,218 18.6% 5.4%
Gisborne 246,757 8.8% 4.8%
Wairoa 146,174 1.1% -2.6%
Hastings 353,255 14.1% 6.2%
Napier 379,698 15.1% 4.6%
Central Hawkes Bay 233,417 12.5% 3.7%
New Plymouth 389,690 8.3% 1.3%
Stratford 217,705 9.4% 1.1%
South Taranaki 188,513 1.5% 0.2%
Ruapehu 141,512 10.2% 1.2%
Whanganui 200,022 10.3% 4.8%
Rangitikei 156,715 9.9% 7.1%
Manawatu 267,894 9.8% 2.9%
Palmerston North 320,134 9.3% 3.6%
Tararua 155,591 2.2% -0.2%
Horowhenua 234,491 12.7% 8.4%
Kapiti Coast 427,620 12.4% 5.2%
Porirua 429,950 12.6% 4.7%
Upper Hutt 373,082 10.6% 6.0%
Hutt 420,972 12.2% 4.6%
Wellington 633,611 15.9% 5.7%
Wellington – Central & South 632,974 14.6% 6.0%
Wellington – East 704,208 17.3% 6.9%
Wellington – North 558,101 16.5% 5.4%
Wellington – West 732,912 16.9% 4.9%
Masterton 249,667 4.7% 0.6%
Carterton 286,582 6.5% 2.4%
South Wairarapa 329,795 8.0% 3.1%
       
Tasman 457,542 8.2% 2.5%
Nelson 464,885 12.0% 3.1%
Marlborough 392,566 11.2% 4.4%
Kaikoura N/A N/A N/A
Buller N/A N/A N/A
Grey 215,864 0.3% 2.1%
Westland 226,176 -1.4% -0.9%
Hurunui 367,915 5.2% 2.0%
Waimakariri 429,832 2.5% 1.5%
Christchurch 492,165 3.5% 0.7%
Christchurch – East 373,972 4.1% 0.8%
Christchurch – Hills 674,056 4.7% 1.2%
Christchurch – Central & North 577,863 4.0% 0.8%
Christchurch – Southwest 465,303 2.7% 0.5%
Christchurch – Banks Peninsula 506,657 2.1% -1.6%
Selwyn 531,517 3.0% -0.2%
Ashburton 351,057 7.4% 1.6%
Timaru 323,296 5.8% 0.1%
MacKenzie 355,551 16.4% 1.9%
Waimate 214,782 7.8% -2.0%
Waitaki 241,912 6.2% 2.9%
Central Otago 390,534 18.3% 6.6%
Queenstown Lakes 910,974 27.0% 8.1%
Dunedin 331,967 11.1% 4.5%
Dunedin – Central & North 348,835 12.2% 6.0%
Dunedin – Peninsular & Coastal 294,844 6.3% 3.8%
Dunedin – South 316,332 11.1% 3.3%
Dunedin – Taieri 342,699 11.7% 3.9%
Clutha 174,420 3.5% -2.2%
Southland 219,557 1.3% 1.0%
Gore 196,117 6.2% 3.0%
Invercargill 223,487 7.8% 2.7%

QV house price index

<!–

var root_url = "http://www.interest.co.nz/charts-csv/";
var tabs_count="2";
var csvfiles_loc= ["/charts-csv/chart_data/real estate/qv-index.csv"," /charts-csv/chart_data/real estate/qv-indexgrowth.csv"];
var chart_title_arr= ["QV house price index","QV house price index growth"];
var chart_subtitles_arr= ["monthly","monthly"];
var tab_titles_arr= ["Index","% change year on year"];
var source_arr= ["QV","QV"];
var source_hyperlink_arr= ["13"];
var tabswidth="13";
var decimal_arr= ["1","1","1"];

// variable declaration
var xpad;
var padding_value=0;
var range_selector=0
var loc;
var updt;
var val_num;
var vi=0;
var max_val;
var x;

var finalAr = new Array();
for (var i = 0; i <= tabs_count; i++) {
finalAr[i] = new Array();
}
var flagAr = new Array();
for (var i = 0; i <= tabs_count; i++) {
flagAr[i] =[];
}
var yaxisAr = new Array();
for (var i = 0; i 536)
{
var b = arr.length – 536; // to get last 36 points
}
else if (arr.length < 536)
{
var b = 537 – arr.length;
}
else if(arr.length == 536)
{
var b=2;
}

// to generate the format for date representation in x axis

var timestamweek = 604800000;
var timestamday = 86400000;
var timestammonth30 = 2592000000;
var timestammonth31 = 2678400000;
var timestamyear = 31536000000;
var timestamquarterly = 7776000000;

for (var u=0;u<2;u++) {
arr[u] = parseLineCSV(arr[u]);
fomat= String(arr[u][0]);
var k=0;

do {
k++;
} while(fomat.charAt(k)!="-")
var k1 =k;

do {
k++;
} while(fomat.charAt(k)!="-")
var k2=k;

do {
k++;
} while(k<fomat.length)
var k3=k;

var dd= fomat.substring(0,k1);
var mm = fomat.substring(k1+1,k2);
var yy = "20"+fomat.substring(k2+1,k3+1);
var yy1=fomat.substring(k2+1,k3+1);

// conversion of months into numerical form

//+++++++++++++++++++++++++++++++++++
if (mm == "Jan")
{
mm= "01";
}
else if (mm == "Feb")
{
mm= "02";
}

else if (mm == "Mar")
{
mm= "03";
}

else if (mm == "Apr")
{
mm= "04";
}

else if (mm == "May")
{
mm= "05";
}

else if (mm == "Jun")
{
mm= "06";
}

else if (mm == "Jul")
{
mm= "07";
}

else if (mm == " Aug")
{
mm= "08";
}

else if (mm == "Sep")
{
mm= "09";
}

else if (mm == "Oct")
{
mm= "10";
}

else if (mm == "Nov")
{
mm= "11";
}

else if (mm == "Dec")
{
mm= "12";
}

// +++++++++++++++++++++++++++++++++++++++++++++++++++++++

var date2 = mm+"/"+dd+"/"+yy;

if (u==0)
{
var timestam1 = Date.parse(date2);
}
else if(u==1)
{
var timestam2 = Date.parse(date2);
}
}
var timestamvar = (timestam2- timestam1);
var flagAR_count=0;
for (var i=2;i<arr.length;i++) {
flagAR_count++;
var tempAr = new Array();
var tempAr1 = new Array();
arr[i] = parseLineCSV(arr[i]);
if (arr[i]!='')
{

fomat= String(arr[i][0]);
replic=String(arr[i][0]);
var k=0;

do {
k++;
} while(fomat.charAt(k)!="-")
var k1 =k;

do {
k++;
} while(fomat.charAt(k)!="-")
var k2=k;

do {
k++;
} while(k<fomat.length)
var k3=k;

var dd= fomat.substring(0,k1);
var mm = fomat.substring(k1+1,k2);
var current_year=""+new Date().getFullYear();
var c_year=Number(current_year.substring(2,4));
var c_data=Number(''+fomat.substring(k2+1,k3+1));
if(c_data c_year)
{
var yy = “19”+fomat.substring(k2+1,k3+1);
}
else if(c_data == c_year)
{
var yy = “20”+fomat.substring(k2+1,k3+1);
}
// conversion of months into numerical form

//+++++++++++++++++++++++++++++++++++
if (mm == “Jan”)
{
mm= “01”;
}
else if (mm == “Feb”)
{
mm= “02”;
}

else if (mm == “Mar”)
{
mm= “03”;
}

else if (mm == “Apr”)
{
mm= “04”;
}

else if (mm == “May”)
{
mm= “05”;
}

else if (mm == “Jun”)
{
mm= “06”;
}

else if (mm == “Jul”)
{
mm= “07”;
}

else if (mm == “Aug”)
{
mm= “08”;
}

else if (mm == “Sep”)
{
mm= “09”;
}

else if (mm == “Oct”)
{
mm= “10”;
}

else if (mm == “Nov”)
{
mm= “11”;
}

else if (mm == “Dec”)
{
mm= “12”;
}
// +++++++++++++++++++++++++++++++++++++++++++++++++++++++

var date = mm+”/”+dd+”/”+yy;

var timestam_1 = Date.parse(date);
var timestam= timestam_1+86400000; //86400000 added to get correct timezone output from Date.parse
var time4 = new Date(timestam);
var Weeko = time4.getDay();
var dd2 = time4.getDate();
var mm2 = time4.getMonth();
var flag_y=arr[i][1];
var ahref_title=”Click here for full story”;
if(arr[i][2]) {
var url=arr[i][2];
var flag_date='{“x”:’+timestam+’, “title”:”  “,”text”:”Click here for Story!”}’;

flagAr[csvgen_counter].push(flag_date);
}
var yy2 = time4.getFullYear();
var yy3 = yy2+””;
var yy4= yy3.substring(2,4);
//++++++++++++++++++++++++this for days conversion++++++++++++++++++++++++++++
if (Weeko==1)
{
Weeko = “Mon”;
}

else if (Weeko==2)
{
Weeko = “Tue”;
}

else if (Weeko==3)
{
Weeko = “Wed”;
}

else if (Weeko==4)
{
Weeko = “Thu”;
}

else if (Weeko==5)
{
Weeko = “Fri”;
}

else if (Weeko==6)
{
Weeko = “Sat”;
}

else if (Weeko==0)
{
Weeko = “Sun”;
}

//+++++++++++++++++++++++this is for month conversion+++++

if (mm2==0)
{
mm2 = “Jan”;
}

else if (mm2==1)
{
mm2 = “Feb”;
}

else if (mm2==2)
{
mm2 = “Mar”;
}

else if (mm2==3)
{
mm2 = “Apr”;
}

else if (mm2==4)
{
mm2 = “May”;
}

else if (mm2==5)
{
mm2 = “Jun”;
}

else if (mm2==6)
{
mm2 = “Jul”;
}

else if (mm2==7)
{
mm2 = “Aug”;
}

else if (mm2==8)
{
mm2 = “Sep”;
}

else if (mm2==9)
{
mm2 = “Oct”;
}

else if (mm2==10)
{
mm2 = “Nov”;
}

else if (mm2==11)
{
mm2 = “Dec”;
}
//++++++++++++++++++++++++++++++++++++++++

//weekly
if ( timestamvar == timestamweek)
{
fomat2=dd2+”-“+mm2+”-“+yy4;
padding_value=timestamweek;
range_selector=2;
}

// Daily
else if ( timestamvar = timestamyear)
{
fomat2=mm2+”-“+yy4;
padding_value=timestamyear;
range_selector=3;
}

//monthly
else if ((timestamvar <= timestammonth30)&&(timestamvar = timestamquarterly)
{
fomat2=mm2+”-“+yy4;
padding_value=timestamquarterly;
range_selector=3;
}

else
{
fomat2=dd2+”-“+mm2+”-“+yy4;
padding_value=timestamday;
range_selector=3;
}

arr[i][0]= fomat2;
var decpad;
decpad = parseFloat(arr[i][1]);
arr[i][1] = decpad;

if(i==(arr.length-1))
{

// Functionality to get the last value

var xvalu=dd2+”-“+mm2+”-“+yy4;
var yvalu= String(arr[i][1]);
var xyvalu=”Latest value at “+xvalu+” is “+yvalu;
updt=”Updated on “+xvalu;
}

tempAr.push(timestam);
if(!arr[i][1])
{
arr[i][1]=null;
}
yaxisAr[csvgen_counter].push(arr[i][1]);
// tempAr1.push(timestam);
// tempAr2.push(tempAr1);
tempAr.push(arr[i][1]);
last_val=timestam;
finalAr[csvgen_counter].push(tempAr);
}
xpad=last_val+padding_value; //*****************to end up graph early//////
if(arr[i]== “”)
{
vi=arr.length-i;
arr.length=arr.length-vi;
i=arr.length-1;
fomat = replic;

var k=0;

do {
k++;
} while(fomat.charAt(k)!=”-“)
var k1 =k;

do {
k++;
} while(fomat.charAt(k)!=”-“)
var k2=k;

do {
k++;
} while(k<fomat.length)
var k3=k;

var dd= fomat.substring(0,k1);
var mm = fomat.substring(k1+1,k2);
var yy = "20"+fomat.substring(k2+1,k3+1);

// conversion of months into numerical form

//+++++++++++++++++++++++++++++++++++
if (mm == "Jan")
{
mm= "01";
}
else if (mm == "Feb")
{
mm= "02";
}

else if (mm == "Mar")
{
mm= "03";
}

else if (mm == "Apr")
{
mm= "04";
}

else if (mm == "May")
{
mm= "05";
}

else if (mm == "Jun")
{
mm= "06";
}

else if (mm == "Jul")
{
mm= "07";
}

else if (mm == "Aug")
{
mm= "08";
}

else if (mm == "Sep")
{
mm= "09";
}

else if (mm == "Oct")
{
mm= "10";
}

else if (mm == "Nov")
{
mm= "11";
}

else if (mm == "Dec")
{
mm= "12";
}
// +++++++++++++++++++++++++++++++++++++++++++++++++++++++

var date = mm+"/"+dd+"/"+yy;
//here flagAr may be
var timestam = Date.parse(date);
var time4 = new Date(timestam);
var Weeko = time4.getDay();
var dd2 = time4.getDate();
var mm2 = time4.getMonth();

var yy2 = time4.getFullYear();
var yy3 = yy2+"";
var yy4= yy3.substring(2,4);
//++++++++++++++++++++++++this for days conversion++++++++++++++++++++++++++++
if (Weeko==1)
{
Weeko = "Mon";
}

else if (Weeko==2)
{
Weeko = "Tue";
}

else if (Weeko==3)
{
Weeko = "Wed";
}

else if (Weeko==4)
{
Weeko = "Thu";
}

else if (Weeko==5)
{
Weeko = "Fri";
}

else if (Weeko==6)
{
Weeko = "Sat";
}

else if (Weeko==0)
{
Weeko = "Sun";
}

//+++++++++++++++++++++++this is for month conversion+++++

if (mm2==0)
{
mm2 = "Jan";
}

else if (mm2==1)
{
mm2 = "Feb";
}

else if (mm2==2)
{
mm2 = "Mar";
}

else if (mm2==3)
{
mm2 = "Apr";
}

else if (mm2==4)
{
mm2 = "May";
}

else if (mm2==5)
{
mm2 = "Jun";
}

else if (mm2==6)
{
mm2 = "Jul";
}

else if (mm2==7)
{
mm2 = "Aug";
}

else if (mm2==8)
{
mm2 = "Sep";
}

else if (mm2==9)
{
mm2 = "Oct";
}

else if (mm2==10)
{
mm2 = "Nov";
}

else if (mm2==11)
{
mm2 = "Dec";
}
//++++++++++++++++++++++++++++++++++++++++

//weekly
if ( timestamvar == timestamweek)
{
fomat2=dd2+"-"+mm2+"-"+yy4;
}

// Daily
else if ( timestamvar = timestamyear)
{
fomat2=mm2+”-“+yy4;
}

//monthly
else if ((timestamvar <= timestammonth30)&&(timestamvar = timestamquarterly)
{
fomat2=mm2+”-“+yy4;
}

arr[i][0]= fomat2;
var decpad;

decpad = parseFloat(arr[i][1]);
//arr[i][1] = roundVal(decpad);
arr[i][1] = decpad;
if(i==(arr.length-1))
{

var xvalu=dd2+”-“+mm2+”-“+yy4;
var yvalu= String(arr[i][1]);
var xyvalu=”Latest value at “+xvalu+” is “+yvalu;
updt=”Updated on “+xvalu;
}
}

}
}

//other required functions

//chart configuration starts here

function getXMLHttpRequest(file) {

//var arrSignatures = [“MSXML2.XMLHTTP.5.0”, “MSXML2.XMLHTTP.4.0″,
//”MSXML2.XMLHTTP.3.0”, “MSXML2.XMLHTTP”,
//”Microsoft.XMLHTTP”];

//for (var i=0; i < arrSignatures.length; i++) {

try
{
var xmlhttp = new window.XMLHttpRequest();
xmlhttp.open("POST",file,false);
return xmlhttp;
}
catch(e)
{
error=e.message;
}

//}
throw new Error("MSXML is not installed on your system.");
}

function readCSV(locfile) {
// load a whole csv file, and then split it line by line
var req = new getXMLHttpRequest(locfile);
//req.open("POST",locfile,false);
req.send("");
return req.responseText.split(/n/g);
}

function parseLineCSV(lineCSV) {
// parse csv line by line into array
var CSV = new Array();

lineCSV = lineCSV.replace(/,/g," ,");

lineCSV = lineCSV.split(/,/g);

// This is continuing of 'split' issue in IE
// remove all trailing space in each field
for (var i=0;i<lineCSV.length;i++) {
lineCSV[i] = lineCSV[i].replace(/s*$/g,"");
}

lineCSV[lineCSV.length-1]=lineCSV[lineCSV.length-1].replace(/^s*|s*$/g,"");
var fstart = -1;

for (var i=0;i=0) {
for (var j=fstart+1;j<=i;j++) {
lineCSV[fstart]=lineCSV[fstart]+","+lineCSV[j];
lineCSV[j]="-DELETED-";

}
fstart=-1;
}
}
fstart = (lineCSV[i].match(/^"/)) ? i : fstart;
}

var j=0;

for (var i=0;i<lineCSV.length;i++) {
if (lineCSV[i]!="-DELETED-") {
CSV[j] = lineCSV[i];
j++;
}

}

return CSV;
}

function roundVal(val_num){
var dec = 2;
var result = Math.round(val_num*Math.pow(10,dec))/Math.pow(10,dec);
return result;
}

function setdecimalpoints(deca)
{
var deca1= deca;
var deca2= parseInt(deca);
if((deca1-deca2)!=0)
{
return 2;
}
else
{
return 0;
}
}
for(i=0;i

Charts loading…
Charts loading…