{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Housing economy, home prices and affordibility\n",
    "\n",
    "Alan Greenspan in 2014 pointed out that there was never a recovery from recession \n",
    "without improvements in housing construction. Here we examine some relevant data, \n",
    "including the Case-Shiller series, and derive an insightful \n",
    "measure of the housing economy, **hscore**, which takes affordibility into account.\n",
    "\n",
    "Contents:\n",
    "\n",
    "- Housing Starts\n",
    "- Constructing a Home Price Index\n",
    "- Real home prices\n",
    "- Indebtedness for typical home buyer\n",
    "- hscore: Housing starts scored by affordability\n",
    "- Concluding remarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Dependencies:*\n",
    "\n",
    "- Repository: https://github.com/rsvp/fecon235\n",
    "- Python: matplotlib, pandas\n",
    "\n",
    "*CHANGE LOG*\n",
    "\n",
    "    2016-02-08  Fix issue #2 by v4 and p6 updates.\n",
    "                   Our hscore index has been completely revised.\n",
    "                   Another 12 months of additional data.\n",
    "    2015-02-10  Code review and revision.\n",
    "    2014-09-11  First version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from fecon235.fecon235 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  Python 2.7.11\n",
      " ::  IPython 4.0.0\n",
      " ::  jupyter 1.0.0\n",
      " ::  notebook 4.0.6\n",
      " ::  matplotlib 1.4.3\n",
      " ::  numpy 1.10.1\n",
      " ::  pandas 0.17.1\n",
      " ::  pandas_datareader 0.2.0\n",
      " ::  Repository: fecon235 v4.16.0123 develop\n",
      " ::  Timestamp: 2016-02-10, 20:12:07 UTC\n",
      " ::  $pwd: /media/yaya/virt15h/virt/dbx/Dropbox/ipy/fecon235/nb\n"
     ]
    }
   ],
   "source": [
    "#  PREAMBLE-p6.15.1223 :: Settings and system details\n",
    "from __future__ import absolute_import, print_function\n",
    "system.specs()\n",
    "pwd = system.getpwd()   # present working directory as variable.\n",
    "print(\" ::  $pwd:\", pwd)\n",
    "#  If a module is modified, automatically reload it:\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "#       Use 0 to disable this feature.\n",
    "\n",
    "#  Notebook DISPLAY options:\n",
    "#      Represent pandas DataFrames as text; not HTML representation:\n",
    "import pandas as pd\n",
    "pd.set_option( 'display.notebook_repr_html', False )\n",
    "#  Beware, for MATH display, use %%latex, NOT the following:\n",
    "#                   from IPython.display import Math\n",
    "#                   from IPython.display import Latex\n",
    "from IPython.display import HTML # useful for snippets\n",
    "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
    "from IPython.display import Image \n",
    "#  e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works\n",
    "from IPython.display import YouTubeVideo\n",
    "#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\n",
    "from IPython.core import page\n",
    "get_ipython().set_hook('show_in_pager', page.as_hook(page.display_page), 0)\n",
    "#  Or equivalently in config file: \"InteractiveShell.display_page = True\", \n",
    "#  which will display results in secondary notebook pager frame in a cell.\n",
    "\n",
    "#  Generate PLOTS inside notebook, \"inline\" generates static png:\n",
    "%matplotlib inline   \n",
    "#          \"notebook\" argument allows interactive zoom and resize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Housing Starts\n",
    "\n",
    "*Housing starts* is an economic indicator that reflects the number of \n",
    "privately owned new houses (technically housing units) on which \n",
    "construction has been started in a given period. \n",
    "We retrieve monthly data released by the U.S. Bureau of the Census. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  In thousands of units:\n",
    "hs = get( m4housing )\n",
    "#         m4 indicates monthly frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  plot( hs )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since housing is what houses people, over the long-term \n",
    "it is reasonable to examine **housing starts per capita**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  US population in thousands:\n",
    "pop = get( m4pop )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Factor 100.00 converts operation to float and percentage terms:\n",
    "hspop = todf((hs * 100.00) / pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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TTbx//nMd03KvveD99z3xfu892Hnn5HXDiLe9hfc3xz/ySH23/uoFF8Af/qD7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\njQLLJd6F4hfvqVNLG6FZi2bLlvSRd/C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wxnQCtwBHBdY5Evg7gDHmeaBRRIYWvaQJVq1a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\nyP8HBIFiNtT4fGYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xacfe28cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( hspop )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**At the peaks, about 1% of the *US population got allocated new housing monthly*.\n",
    "The lowest point shown is after the Great Recession at 0.2%.**\n",
    "\n",
    "Clearly there's a downward historical trend, so to discern **short-term housing cycles**,\n",
    "we detrend and normalize hspop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  regresstime slope = -0.000742599274435\n"
     ]
    },
    {
     "data": {
      "image/png": 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b7XiE6ZL0u/GRqUiP7duBxx7LL0VlJtG2HkdzlMn558vQZPX1ud006eqq/M7W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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabf2874c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(detrendnorm( hspop ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Surprisingly, housing starts per capita during the Great Recession did not\n",
    "exceed two standard deviations on the downside. \n",
    "\n",
    "2015-02-10 and 2016-02-08:  It appears that housing starts has recovered relatively\n",
    "and is back to mean trend levels.\n",
    "\n",
    "In the concluding section, we shall derive another measure of housing activity\n",
    "which takes affordibility into account."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructing a Home Price Index\n",
    "\n",
    "The correlation between Case-Shiller indexes, 20-city vs 10-city, is practically 1.\n",
    "Thus a mash-up is warranted to get data extended back to 1987.\n",
    "Case-Shiller is not dollar denominated (but rather a chain of changes)\n",
    "so we use the median sales prices from 2000 to mid-2014 released by the\n",
    "National Association of Realtors to estimate home price,\n",
    "see function **gethomepx** for explicit details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature: \u001b[0m\u001b[0mgethomepx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfredcode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'm4homepx'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mSource:\u001b[0m\n",
       "def gethomepx( fredcode=m4homepx ):\n",
       "     '''Make Case-Shiller 20-city, and try to prepend 1987-2000 10-city.'''\n",
       "     #  Fred's licensing may change since source is S&P, \n",
       "     #  however, we have a local copy of 1987-2013 monthly SA data.\n",
       "     hpnow = getdata_fred( 'SPCS20RSA' )\n",
       "     #                          20-city home price index back to 2000-01-01.\n",
       "     try:\n",
       "          hpold = readfile( 'FRED-home-Case-Shiller_1987-2013.csv.gz', compress='gzip' )\n",
       "          #                 ^includes 10-city index from 1987-2000.\n",
       "          #                  Current correlation with 20-city: 0.998\n",
       "          #                  Thus the mashup is justified.\n",
       "          hpall = hpold.combine_first( hpnow )\n",
       "          #             ^appends dataframe\n",
       "          print(' ::  Case-Shiller prepend successfully goes back to 1987.')\n",
       "     except:\n",
       "          hpall = hpnow\n",
       "          print(' ::  Case-Shiller since 2000 (1987-archive not found).')\n",
       "     #  Case-Shiller is not dollar based, so we use:\n",
       "     #  Median Sales Price of Existing Homes\n",
       "     #  from the National Association of Realtors, fredcode: HOSMEDUSM052N\n",
       "     dollarindex = 183700.57 / 153.843\n",
       "     #     means:  ^Realtor$   ^C-S 20-city from 2000-01-01 to 2014-06-01.\n",
       "     return hpall * dollarindex\n",
       "\u001b[1;31mFile:      \u001b[0m~/Dropbox/ipy/fecon235/lib/yi_fred.py\n",
       "\u001b[1;31mType:      \u001b[0mfunction"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  We can use ? or ?? to extract code info:\n",
    "gethomepx??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  Case-Shiller prepend successfully goes back to 1987.\n"
     ]
    }
   ],
   "source": [
    "#  Our interface will not ask the user to enter such messy details...\n",
    "homepx = get( m4homepx )\n",
    "#             m4 indicates monthly home prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xP/7o7vw1ZQo0bBh2GlPorKE5D6zOMv/imr2s3A8/7EZBjXqBUEj7PA4id52C\niDwrIktFZEZg2p0islBEpvrH6YF5t4nIHBGZKSLtA9NbisgMP++xwPRqIjLcT58oIvsF5vUQkdn+\nUYlvOmgK3fLl8MQT7obzxoQpZfWRiLQB1gCDVfVQP60PsFpVHy617EHAUOBoYF/gHaCxqqqITAKu\nU9VJIjIa6KeqY0SkF3CIqvYSkQuAzqraVURqApOBln71U4CWqrqy1Dat+sjE3u23u+qjJ58MO4mp\nLDKuPlLVD4AVZa2zjGkdgWGqulFV5wFfA61FpA5QQ1Un+eUGA53887OBQf75COAU//w0YKyqrvQF\nwTigQ6q8xsTN8uWuMLAB70wUVKRN4Q8i8pmIPCMiJQP61gWCdzJdiDtjKD19kZ+O/3cBgKpuAlaJ\nSK0k64osq7PMv7hmD+bu2xe6dInPcBaFsM/jJC7XKTwO3O2f3wM8BFyelUQZ6NmzJw1961xRUREt\nWrSgbdu2wLYdaq8Tv542bVqk8pTn9bRp0yKVJ93XJYYOLeaJJ2DmzGjlK9TvSxxfZ2t/FxcXM3Dg\nQICtx8uypNUlVUQaAq+XtCkkmicivQFU9QE/bwzQB5gPTFDV5n56N+AEVb3WL3Onqk4UkarAYlXd\nS0S6Am1V9Rr/nieB8ao6vNT2rU3BxFaXLnD44W7wO2PyKatdUn0bQYnOQEnPpFFAVxHZSUT2BxoD\nk1R1CfCziLQWEQEuAUYG3lNyQf95wLv++VigvYgUicgeQDvg7UzyGhNF778PkybBzTeHncSYbdLp\nkjoM+DfQVEQWiMhlwN9FZLqIfAacCNwIoKpfAi8CXwJvAb0CP+N7AU8Dc4CvVXWMn/4MUEtE5gD/\nA5ScbfyEq5qaDEwC7ird8yhqSlcNxEVcc0N8s48fX8xNN7mb6OyyS9hpyieu+9xypydlm4Kqditj\n8rNJlr8PuK+M6VOAX1U/qep64PwE6xoADEiV0Zi4GTHC3bqxa9ewkxizPRvmwpg8GzoUbrkFPvzQ\n3fTdmDAkalOwUVKNyZPVq+Huu93Q2G+/bQWCiSYb+yiLrM4y/+KSfe5caNUKlixxd1Vbvrw47EgZ\ni8s+L81yp8cKBWNy7Jtv4KST4LrrYMgQqFMn9XuMCYu1KRiTQ19/DSef7K5DuOqqsNMYs40NnW1M\nnn35pRUIJn6sUMgiq7PMv6hmf/NNaNsW/va3sguEqOZOR1yzW+70WO8jY7Jowwbo3dvdY3nkSPjt\nb8NOZEybq4rWAAAVJ0lEQVT5WJuCMVmyeTOcfz6sWweDB0OtWmEnMiYxu07BmBzr29fdKOftt6Fa\ntbDTGJMZa1PIIquzzL+oZJ82zd1jefDg9AqEqOTORFyzW+70WKFgTAVt3Ajdu8NDD0GDBmGnMaZi\nrE3BmAp65BFXZfTWWyBl3aTWmAhK1KZghYIxFbB0KRxyiBvcrmnTsNMYkz67eC0PrM4y/8LO3rs3\n9OxZ/gIh7NwVEdfsljs91vvImAyNHQvvvguffx52EmOyx6qPjMnA4sVw9NEwcCCcemrYaYwpP6s+\nMiZLVq+Gzp3hyiutQDCFxwqFLLI6y/zLd/Y1a+CMM+Cww+COOzJfj+3z/LPc6bFCwZg0lRQIzZrB\nE09AFfvrMQXI2hSMSYOqKxDq1oV//csKBBN/NvaRMRUwbJi7lebrr1uBYAqbfb2zyOos8y8f2Vet\ngltugX/+E6pm6WeU7fP8s9zpsULBmBT69IHTT7d7I5jKwdoUjEli2jRo397dWnPPPcNOY0z22HUK\nxpTTpk1w9dVw771WIJjKwwqFLLI6y/zLZfYHH4QaNeCKK7K/btvn+We502O9j4wpw/Tp7qY5U6ZY\nbyNTuVibgjGlLFkCxx/vrlju0SPsNMbkhrUpGJOGjRvhnHPg4outQDCVkxUKWWR1lvmX7ey33Qa7\n7QZ//WtWV/srts/zz3Knx9oUjPGeegpGjoSJE60dwVRe1qZgDO6GOd27wwcfQOPGYacxJvds7CNj\nEvj8c9eG8MorViAYYyfJWWR1lvlX0exLl8JZZ8Ejj7geR/lSmfd5WCx3eqxQMJXWL79Ax46ul9FF\nF4WdxphosDYFUylt2QIXXugalJ9/HuRXNavGFDZrUzDGU4Ubb4QFC+Ddd61AMCbIqo+yyOos8y+T\n7HfeCR9/DG++CTvvnPVIaals+zwKLHd67EzBVCpDh8KAATB5MhQVhZ3GmOixNgVTabzwgqs2eucd\nOPjgsNMYEy4b+8hUWqrQr58rEMaOtQLBmGSsUMgiq7PMv1TZP/gAjj0WhgyBjz6CQw/NT65UCnmf\nR5XlTo8VCqYgrV/v7pp28cXw+9/DJ5/AAQeEncqY6LM2BVNw1q2Dzp1dz6JBg9yop8aY7dl1CqZS\n2LABzj3X9SwaMgSq2jfcmHKx6qMssjrL/Atm37gRunaFatVg8OBoFwiFss/jxHKnJ2WhICLPishS\nEZkRmFZTRMaJyGwRGSsiRYF5t4nIHBGZKSLtA9NbisgMP++xwPRqIjLcT58oIvsF5vXw25gtIt2z\n85FNIdqwAS64wP07bBjsuGPYiYyJp5RtCiLSBlgDDFbVQ/20vsByVe0rIrcCe6hqbxE5CBgKHA3s\nC7wDNFZVFZFJwHWqOklERgP9VHWMiPQCDlHVXiJyAdBZVbuKSE1gMtDSR5kCtFTVlaXyWZtCJbd2\nrRvQbssWeOkld6ZgjEku4+sUVPUDYEWpyWcDg/zzQUAn/7wjMExVN6rqPOBroLWI1AFqqOokv9zg\nwHuC6xoBnOKfnwaMVdWVviAYB3RIlddULt9/D8ccA3vsAS+/bAWCMRWVaZtCbVVd6p8vBWr753WB\nhYHlFuLOGEpPX+Sn4/9dAKCqm4BVIlIryboiy+os8+vbb+Hoo4vp1g2efRZ22insROmL6z6H+Ga3\n3OmpcFOcrxoKtf6mZ8+eNGzYEICioiJatGhB27ZtgW07NNevmzdvyxdfwMcfF7P77nDNNW2pUiV/\n26/I62nTpkUqTzqva9ZsyxlnwDHHTOPYY0EkWvlSvS4RlTyF/n2J8+ts7e/i4mIGDhwIsPV4WZa0\nrlMQkYbA64E2hZlAW1Vd4quGJqhqMxHpDaCqD/jlxgB9gPl+meZ+ejfgBFW91i9zp6pOFJGqwGJV\n3UtEuvptXOPf8yQwXlWHl8oWWpvCsmUwYoQbU2f6dGjSxFVjfPedG6f/nnvcTVxsaObs+uAD1+20\nXz/X28gYU37ZHvtoFNDDP+8BvBaY3lVEdhKR/YHGwCRVXQL8LCKtRUSAS4CRZazrPOBd/3ws0F5E\nikRkD6Ad8HaGebNi82Z3wB84EDp0cPfzff99uOkmWLwYJk6Et95y9/y97z7o0wdatYK333bj75iK\n2bIF/v53VyA895wVCMbkhKomfQDDgO+BDbi6/0uBmrieRbNxB++iwPK34xqYZwKnBaa3BGb4ef0C\n06sBLwJzgIlAw8C8S/30OUCPBPk011auVL31VtW99lKtU0e1UyfVF15QXbNm++UmTJiw3evNm1WH\nD1dt3Fi1fXvVzz/PedSMlM4dNZs2uf3dqpXq8cerzp+/bV7UsycS19yq8c1uubfnj52/OqambFNQ\n1W4JZp2aYPn7gPvKmD4F+NVwZKq6Hjg/wboGAANSZcyluXPh9NPht791ZwLlGT+nShU4/3zo1Ake\nfxxOOgnOOw/uugv22it3mQvFpk2uau7ee6FmTfjTn9y+3GGHsJMZU7hs7KMk1q+H1q2he3dXRVRR\nP/3kCoThw11/+jZtKr7OQqMKM2a4wmDIEFcI//WvcPLJ1jZjTDYlalOwQiGBFSugSxeoXdvVX2fz\ngDR2rLvY6rbb3Bj/JevesgW++ALmzXNnGTVquDOKXXZxg7vtvrt7XmhWr3Y3vnnrLRg92l1r0Lkz\n9OgRnaGujSk0ViiUw5w5cOaZ7tG3b/rVFcXFxVu7gqUyb56rCjnySNdo/fHHMHKkKwyaNnXLrFrl\nejitWwe//OJeV6sGe+8NLVq4A2fbtlC3bsUKrfLkBvdrvrzb27zZfeZvv3Wf56ef4Kuv3JDWkye7\n6rkzznBVdU2apL/+8maPirjmhvhmt9zbs1FS01Rc7Hq13HMPXHll7rbTsCFMmOAKnSFD3I1gXn4Z\njjgi8QFR1RUMS5a4g+nQoXDzzW6Yh2bN3KNJE6he3V3IdcIJ7pd2ugfYzZth/nzXjjJ3LixY4LYV\nfGzYACtXumqdDh1codaokav/X7HCPVau3PbvsmXu4D9rFuy5Jxx4oDvbKSpyeW+6yRVsu+6arT1r\njKkIO1MIeO45d5AdNszVYcfFihXuoDtzJsye7X6Jr13rqqnAnVUUFbnhII45xlVJ7bADfPONOyua\nM8ddZ/Hhh67K6sAD3UG/fn2oUwf22cc9atd2Zyo1argD/dixrvvtN9+4QqioyF2nEfy3Vi138G/e\n3L3PGBMNVn2UwmOPwUMPwZgxcNBBWQgWAaru4P3ll6665qOP4NNP3fNNm9yBv1Ejd73FwQe7hu99\n9gk7tTEmHxIVCimvU4j6gyxcp/DII6qNGqnOm1ex9Vg/6PyLa/a45laNb3bLvT0yvU6h0D33HDz8\nsKs6adAg7DTGGBOuSl199OqrcO21MH584VQZGWNMOrI99lGsbd7sev1ce63rF28FgjHGOJWuUFiw\nAE49Fd5803XrPPLI7K279LDIcRHX3BDf7HHNDfHNbrnTU2kKhS1b4KmnoGVLaN/eVRntt1/q9xlj\nTGVSKdoUli1zA9Ft2OAKBhs6wRhT2VXaNoVvv4XjjnN98D/6yAoEY4xJpqALhc8+c4XBDTe44Zer\n5PjTWp1l/sU1e1xzQ3yzW+7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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabdaaaec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Case-Shiller is seasonally adjusted:\n",
    "plot( homepx )\n",
    "#  so the plot appears relatively smooth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3.7, 3.73, 2.52, 12, 347, '1987-01-01', '2015-11-01']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Geometric rate of return since 1987:\n",
    "georet( homepx, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first element tells us home prices have increased\n",
    "approximately 3.7% per annum.\n",
    "The third element shows price volatility of 2.5%\n",
    "which is very low compared to other asset classes.\n",
    "\n",
    "But this does not take into account inflation.\n",
    "In any case, recent home prices are still below\n",
    "the levels just before the Great Recession."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real home prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  This is an synthetic deflator created from four sources:\n",
    "#  CPI and PCE, both headline and core:\n",
    "defl = get( m4defl )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  \"Real\" will mean in terms of current dollars:\n",
    "homepxr = todf( homepx * defl )\n",
    "#     r for real "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GIrI/UAjMUNWVwAYR6ejrPB94IXBOX79/FvCK358CdBORAhFpgnsTeTnzj5x/kq16XIir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\n61S1wO8LsE5VG4vIAABVvd3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693cr1L34ogWWDaO6VGYU0rqPRGQs0AVoJiJLgZtU9ZFA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\nMwqGYRjGVv4feFmGlK0gMecAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabd5d6ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( homepxr )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.32, 1.35, 2.54, 12, 347, '1987-01-01', '2015-11-01']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Real geometric return of home prices:\n",
    "georet( homepxr, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Real* home prices since 1987 have increased at the approximate\n",
    "rate of +1.3% per annum.\n",
    "\n",
    "Note that the above does not account for annual property taxes\n",
    "which could diminish of real price appreciation.\n",
    "\n",
    "Perhaps home prices are only increasing because new stock of housing\n",
    "has been declining over the long-term (as shown previously).\n",
    "\n",
    "The years 1997-2006 is considered a **housing bubble**\n",
    "due to the widespread availability of *subprime mortgages*\n",
    "(cf. NINJA, No Income No Job Applicant, was often not rejected.)\n",
    "**Median home prices *doubled* in real terms**: from \\$140,000 to \\$280,000.\n",
    "\n",
    "**Great Recession took down home prices** (180-280)/280 = **-36% in real terms.**\n",
    "\n",
    "2015-02-10: we are roughly at 200/280 = 71% of peak home price in real terms.\n",
    "\n",
    "2016-02-08: we are roughly at 220/280 = 79% of peak home price in real terms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indebtedness for typical home buyer\n",
    "\n",
    "For a sketch, we assume a fixed premium for some long-term mortgages over 10-y Treasuries,\n",
    "and then compute the number of hours needed to \n",
    "pay *only the interest on the full home price* (i.e. no down payment assumed).\n",
    "\n",
    "This sketch does not strive for strict veracity, but simply serves as an\n",
    "indicator to model the housing economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mortpremium = 1.50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mortgage = todf( get(m4bond10) + mortpremium )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Yearly interest to be paid off:\n",
    "interest = todf( homepx * (mortgage / 100.00) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Wage is in dollars per hour:\n",
    "wage = get( m4wage )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Working hours to pay off just the interest:\n",
    "interesthours = todf( interest / wage )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Mortgage interest to be paid as portion of ANNUAL income,\n",
    "#  assuming 2000 working hours per year:\n",
    "payhome = todf( interesthours / 2000.00 )\n",
    "\n",
    "#  We ignore tiny portion of mortgage payment made towards reducing principal.\n",
    "#  And of course, the huge disparity in earned income among the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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fK1bk9zyffFK1s7ROHaioSFwnw+Ef1qyxTJNYfvc7eOopeyj7Bdfy9hclGbwL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\niep5u5a3w+EoOvJhm+zcCd9+axMt1Ja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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabd28b8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( payhome )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we assume 2000 hours worked per year (40 hours for 50 weeks), we can see that\n",
    "interest payment can potentially take up to 50% of total annual pre-tax income. \n",
    "\n",
    "2015-02-10: Currently that figure is about 20% so housing should be affordable,\n",
    "but the population is uncertain about the risk on taking on debt.\n",
    "(What if unemployment looms in the future?) \n",
    "\n",
    "Prospects of deflation adds to the fear of such risk.\n",
    "Debt is best taken on in inflationary environments.\n",
    "\n",
    "The housing bubble clearly illustrated that\n",
    "huge *price risk* of the underlying asset could be an important consideration.\n",
    "\n",
    "Thus the renting a home (without any equity stake) may appear preferable over buying a home."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  #  Forecast payhome for the next 12 months:\n",
    "#  forecast( payhome, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2016-02-09: Homes should be slightly more affordable: 19% of annual income -- perhaps\n",
    "due to further declining interest rates, or even some\n",
    "increase in wages for the typical American worker.\n",
    "\n",
    "Caution: although the numbers may indicate increased affordability,\n",
    "it has become *far more difficult to obtain mortgage financing due to\n",
    "strict credit requirements*. The pendulum of scrutiny from the NINJA days of the\n",
    "subprime era has swung to the opposite extreme.\n",
    "Subprime mortgages were the root cause of the Great Recession.\n",
    "This would require another notebook which studies credit flows\n",
    "from financial institutions to home buyers.\n",
    "\n",
    "Great Recession: There is evidence recently that families shifted to home rentals,\n",
    "avoiding home ownership which would entail taking on mortgage debt.\n",
    "Some home owners experienced negative equity.\n",
    "And when the debt could not be paid due to wage loss, it seemed reasonable to\n",
    "walk away from their homes, even if that meant damage to their credit worthiness.\n",
    "*Housing construction had to compete with a large supply of foreclosed homes on the market.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## hscore: Housing starts scored by affordability\n",
    "\n",
    "The basic idea here is that housing starts can be weighted by some\n",
    "proxy of \"affordability.\"\n",
    "An unsold housing unit cannot be good for a healthy economy.\n",
    "\n",
    "Recall that our variable *payhome* was constructed as a function of\n",
    "home price, interest rate, and wage income -- to solve for the portion\n",
    "of annual income needed to pay off a home purchase -- i.e. indebtedness.\n",
    "\n",
    "**Home affordability** can thus be *abstractly* represented as 0 < (1-payhome) < 1,\n",
    "by ignoring living expenses of the home buyer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "afford = todf( 1 - payhome )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  hspop can be interpreted as the percentage of the population allocated new housing.\n",
    "\n",
    "#  Let's weight hspop by afford to score housing starts...\n",
    "hscore = todf( hspop * afford )\n",
    "\n",
    "#  ... loosely interpretated as new \"affordable\" housing relative to population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NzO1kyuDdXfmnXcuEzWz9dOea+60YUGFvbtbxUqqqtBQy27vAnTthyRItWSwu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\nSukzHlK30UYG9mFaKo6BV0uqOACS5gHH2b41tUsnSNqHUOe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      "text/plain": [
       "<matplotlib.figure.Figure at 0xabd5bfcc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( hscore )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Y\n",
      "count  347.000000\n",
      "mean     0.313257\n",
      "std      0.091287\n",
      "min      0.124288\n",
      "25%      0.244456\n",
      "50%      0.316766\n",
      "75%      0.387574\n",
      "max      0.483924\n"
     ]
    }
   ],
   "source": [
    "stat( hscore )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**hscore** can be roughly interpreted as \"affordable\" housing starts\n",
    "expressed as percentage of the total U.S. population.\n",
    "\n",
    "The overall mean of *hscore* is approximately 0.31, and we observe a band between\n",
    "0.31 and 0.47 from 1993 to 2004.\n",
    "That band could  be interpreted as an equilibrium region for the housing economy\n",
    "(before the Housing Bubble and Great Recession).\n",
    "It's also worth noting that long-term interest rates during that epoch was\n",
    "determined by the market -- yet untouched by the massive *quantitative easing*\n",
    "programs initiated by the Federal Reserve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Forecast\n",
       "0   0.295048\n",
       "1   0.297774\n",
       "2   0.300460\n",
       "3   0.303146\n",
       "4   0.305832\n",
       "5   0.308518\n",
       "6   0.311204\n",
       "7   0.313890\n",
       "8   0.316576\n",
       "9   0.319262\n",
       "10  0.321948\n",
       "11  0.324634\n",
       "12  0.327320"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Forecast for hscore, 12-months ahead:\n",
    "forecast( hscore, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concluding remarks\n",
    "\n",
    "We created an index **hscore** which expresses new \"affordable\" housing units\n",
    "as percentage of total population. Affordability was crudely modeled by a few\n",
    "well-known economic variables, plus our extended Case-Schiller index\n",
    "of median home prices.\n",
    "\n",
    "- 2016-02-09  Following the Great-Recession lows around 0.13, *hscore* has now reverted to its long-term mean of 0.31, *confirming the recovery*, and is forecasted to slightly increase to 0.33.\n",
    "\n",
    "\n",
    "- The Fed terminated its QE program but has not sold off any of its mortgage securities. That reduces upward pressure on mortgage rates. However, our *hscore* supports the Fed's rate hike decision on 2015-12-16 since it gives evidence that the housing market has recovered midway between the housing bubble and the subprime mortgage crisis."
   ]
  }
 ],
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