Financial planning, especially for those in tech looking to buy a house.
Feel free to submit an issue or PR if you find areas for improvement.
pip install -r requirements.txtpython mortgage.py --cfg settings.ymlThe key columns to look at here are net_worth_mortgage and net_worth_rent. The other columns break down the components of net worth. The net_worth_mortgage column adds all values except savings_rent. If you have substantial savings or investments that you want to be counted in the net worth computation, then specify the parameters in settings.yml placed under the "Current net-worth settings" title.
Annual results:
savings_mortgage house_profit savings_rent investments roth 401k net_worth_mortgage net_worth_rent
2023 116569.66 0.00 116569.66 100000.0 3342.92 11571.66 231484.25 231484.25
2024 146645.42 0.00 146645.42 100000.0 10730.79 37145.04 294521.24 294521.24
2025 148272.08 -10148.65 178940.73 100000.0 19136.57 66241.98 323501.98 364319.28
2026 172662.19 2393.69 213585.02 100000.0 28666.59 99230.49 402952.95 441482.10
2027 198766.27 15447.02 250714.56 100000.0 39437.03 136512.80 490163.13 526664.40
2028 229131.08 29032.17 290472.73 100000.0 51574.87 178528.41 588266.53 620576.01
2029 261838.57 43170.79 333010.40 100000.0 65218.77 225757.29 695985.42 723986.46
2030 296796.04 57885.45 378486.33 100000.0 80520.10 278723.42 813925.01 837729.85
2031 334131.16 73199.60 427067.52 100000.0 97644.04 337998.60 942973.41 962710.17
2032 373978.23 89137.68 478929.63 100000.0 116770.79 404206.57 1084093.26 1099906.98
2033 416478.45 105725.09 534257.40 100000.0 138096.82 478027.45 1238327.81 1250381.66
2034 461780.32 122988.31 593245.10 100000.0 161836.32 560202.64 1406807.59 1415284.06
2035 510039.97 140954.85 656097.00 100000.0 188222.68 651540.03 1590757.53 1595859.71
2036 561421.55 159653.38 723027.87 100000.0 217510.12 752919.66 1791504.72 1793457.65
2037 616097.60 179113.72 794263.45 100000.0 249975.52 865299.89 2010486.73 2009538.86
2038 674249.45 199366.90 870041.03 100000.0 285920.28 989724.05 2249260.67 2245685.36
2039 736072.11 220445.22 950610.00 100000.0 325672.42 1127327.62 2509517.37 2503610.04
2040 857449.05 242382.31 1036232.42 100000.0 369588.86 1279346.04 2848766.25 2785167.31
2041 987358.43 265213.15 1127183.65 100000.0 418057.79 1447123.13 3217752.50 3092364.58
2042 1126288.64 288974.15 1223753.01 100000.0 471501.40 1632120.22 3618884.41 3427374.64
2043 1205106.41 301215.25 1280049.19 100000.0 500230.60 1731567.47 3838119.73 3611847.27
Monthly results:
savings_mortgage house_profit savings_rent investments roth 401k net_worth_mortgage net_worth_rent
2023-07-01 102486.33 0.00 102486.33 100000.0 541.67 1875.00 204903.00 204903.00
2023-08-01 105301.54 0.00 105301.54 100000.0 1089.46 3771.20 210162.20 210162.20
2023-09-01 108118.47 0.00 108118.47 100000.0 1643.43 5688.79 215450.68 215450.68
2023-10-01 110936.17 0.00 110936.17 100000.0 2203.63 7627.94 220767.74 220767.74
2023-11-01 113753.62 0.00 113753.62 100000.0 2770.11 9588.84 226112.56 226112.56
... ... ... ... ... ... ... ... ...
2043-02-01 1152407.15 293027.39 1242465.99 100000.0 480923.97 1664736.83 3691095.35 3488126.80
2043-03-01 1165527.05 295064.15 1251845.19 100000.0 485692.52 1681243.35 3727527.08 3518781.07
2043-04-01 1178684.98 297107.70 1261236.96 100000.0 490499.62 1697883.31 3764175.60 3549619.89
2043-05-01 1191878.86 299158.06 1270639.10 100000.0 495345.55 1714657.69 3801040.15 3580642.33
2043-06-01 1205106.41 301215.25 1280049.19 100000.0 500230.60 1731567.47 3838119.73 3611847.27where
net_worth_mortgage = savings_mortgage + house_profit + investments + roth + 401k
net_worth_rent = savings_rent + investments + roth + 401k
These numbers are visualized below (plots conducted inside mortgage.py):
Note: if you are curious about the estimations from more specific components of the savings_mortgage or savings_rent (etc.), more resolute termainal printouts are available if you increase the verbose parameter in your settings.yml file.
Using settings_optimize.yml (for example), changes to values on the parameters within settings.yml can be conducted to observe how it effects net worth.
python mortgage.py --cfg settings_optimize.ymlFor example, one can grid search the best loan duration, down payment, and optimal time to purchase a home.
amount_down loan_duration housePurchase_year_investigate housePurchase_month_investigate net_worth_mortgage net_worth_rent
480 100000.0 10 2024 3 4350713.50 3896640.04
400 90000.0 10 2024 3 4342795.12 3896640.04
320 80000.0 10 2024 3 4334876.66 3896640.04
481 100000.0 10 2024 6 4332128.38 3896640.04
240 70000.0 10 2024 3 4326958.16 3896640.04
401 90000.0 10 2024 6 4324209.99 3896640.04
160 60000.0 10 2024 3 4319039.97 3896640.04
321 80000.0 10 2024 6 4316291.54 3896640.04
482 100000.0 10 2024 9 4313606.43 3896640.04
80 50000.0 10 2024 3 4308761.91 3896640.04
241 70000.0 10 2024 6 4308373.04 3896640.04
402 90000.0 10 2024 9 4305688.04 3896640.04
161 60000.0 10 2024 6 4300454.85 3896640.04
0 40000.0 10 2024 3 4298484.79 3896640.04
322 80000.0 10 2024 9 4297769.59 3896640.04
483 100000.0 10 2024 12 4295147.02 3896640.04
81 50000.0 10 2024 6 4290176.79 3896640.04
242 70000.0 10 2024 9 4289851.09 3896640.04
540 100000.0 30 2024 3 4288611.46 3896640.04
500 100000.0 15 2024 3 4287711.94 3896640.04
403 90000.0 10 2024 12 4287228.63 3896640.04
162 60000.0 10 2024 9 4281932.90 3896640.04
1 40000.0 10 2024 6 4279899.66 3896640.04
323 80000.0 10 2024 12 4279310.18 3896640.04
541 100000.0 30 2024 6 4278525.49 3896640.04The results displayed in the directory (results/settings_optimize.yml/) are the setting with the highest net_worth_mortgage.