There are a lot of personal finance related calculators out there, but there are only a handful that I would recommend using on a regular basis. Here are a few of my favorite tools that have easy to use options and clear results.
This rent vs buy calculator balances all the costs you could thing of related to buying a home versus the monthly rental costs. One of this calculator’s great features is that it accounts for the opportunity cost of the mortgage down payment. The opportunity cost is the cost of what you could have earned from that money if you hadn’t bought the house. The calculator also builds in costs to account for rising rent prices, home appreciation, and inflation. Also, the controls are easy to adjust how long you plan on staying and your home price budget. I know it sounds like a lot of options, but if you aren’t sure about one of them just leave it at the default value.
This is my go to retirement calculator. I use it as a simple and quick check to make sure that my savings rates are high enough to meet my retirement goals. The calculator is completely free and doesn’t require registration or anything like that. It automatically does not include Social Security, so you have to manually key in a number for that. In general, I like to pretend that Social Security will be tiny by the time I retire. I just put in $1,000 a month at most ($2,000 if you’re married) for a very conservative estimate of how much SS would actually pay out.
This calculator has pretty similar results to the previous Vanguard one, but it’s tilted more for figuring out how quickly you can retire. I really like the chart on this one, and that it emphasizes controlling spending in order to retire more quickly.
This is a great spreadsheet to help you understand that components of your savings rate in order to calculate it. It’s not as spiffy as some of the other tools, but it’s pretty straightforward and has a very detailed breakdown of how to tally up your income and savings.
Last year I moved into a city which had much older housing stock than other cities that I had lived in before. I was under-prepared for the housing market here, because I had not researched the factors that you should look into for older homes which vary a lot depending on the era that they were built in. I created this interactive mobile friendly map to visually help show the differences in regions and counties.
Now that I’ve updated all the tools to reflect 2013 SCF data, I decided to create a much larger database from the SCF using data all the way back to 1989. There’s more to come on that, as I am still working on the best way to let you guys play with the data, but just to show you what’s to come. Like the tools that are currently available, this data was calculated using the Federal Reserve’s Survey of Consumer Finances. I took the net worth statistics for each year and calculated what it would take to rank at certain percentiles of wealth.
Here is a table showing the net worth by percentile for every SCF since 1989. The Fed has already converted all the dollar values in the most recent versions of the data to 2013 dollars (using standard inflation rates), so we’re comparing apples to apples. As you can see across the board, from 2004 to 2007, net worth peaked across the distribution. The disturbing thing though, is that since then only the 90% and above has had a nearly full recovery in wealth. The 25% and below are hitting historic lows in wealth, and the median wealth of Americans is lower than 1989, despite the massive increase in GDP, productivity, and total wealth in the United States. It appears that the majority of those gains have been concentrated in the top 10% of Americans rather than being evenly distributed.
For further reference here are those same numbers plotted to show the relative changes in wealth between Americans at the 10th, 25th, 50th, 75, and 90th percentiles. The largest gains in the last couple decades have captured at the 10% and presumably above. I am hesitant to calculate and publish the numbers of the 5th and 95th percentiles because the data is a lot more prone to being skewed at the long tail ends of the distributions.