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Pieces Of Creation Logo version 2
 

This is my 998th post at CM! Two more to the 4-figure milestone!

 

In my superhero campaign, the PCs are currently shopping for a building to convert into a base of operations for a second set of superhero/civilian Identities that UNTIL has prompted them to create so that they can deal with problems that they (and the UN) can’t officially be seen to be interfering in, but that are too serious for them not to deal with – spy- and political games within games, always lots of “fun”!

To some extent, I’m using this search as a plot vehicle, a way to add to the richness of the game world; to some extent, I’m using it to highlight existing riches that have largely gone unnoticed until now. And, of course, having a campaign-within-a-campaign provides endless opportunities for the campaigns to step on each other’s toes in-game, providing fresh challenges for the players and their characters.

It’s also posing new problems for me to solve as a GM. If you’re going to sell the PCs a building (and the land that it sits on), you need to have some idea of how much such things will cost, for example.

I’m currently a LONG way ahead of the game-play front in terms of plotting, so I spent most of the weekend just passed in creating a system to solve that very question.

Today’s article will examine the methodology and results before offering them as a 15-page free PDF download for other GMs to use.

I want to demonstrate how the work was done so that other GMs can not only extend or expand the system as necessary, but can satisfy similar needs in their own campaigns, regardless of genre.

Before we’re done, I’ll demonstrate how I intend to use the system in my campaign, and how it can be adapted to suit other campaigns.

I think I’ve caught and corrected any errors, but this will also equip you to make any corrections that may be necessary. Nor have I been entirely 100% consistent in some areas, because I was problem-solving as I went. I can live with that, but if you want to make the corrections, this will equip you to do so.

In addition to offering the results as a PDF, I am providing them in two spreadsheet formats to facilitate such corrections.

Today has been a public holiday here in Sydney, so I’m starting this late and intend to publish it a day later than usual.

That takes all the time pressure off, so that I don’t have to rush any aspect of the planned article.

Contents:

  1. Production: Research
  2. Production: Process
  3. Production: Conversions
  4. Production: Execution

    The first half of the article is all about how the PDF’s content was created, and will culminate in links that will enable you to download the results.

  5. Usage
  6. Example
  7. Extension
  8. Adaption

    The second half will deal with usage of the system, extending it as may be desired, and adapting it to other circumstances, even if the latter can only provide guidelines.

Production: Research

I’ve been curious about, and interested in, the pricing of real estate for a long time. I’ve raised the subject in conversation with a number of real estate agents as social contact permitted.

I had always assumed that there was some magic formula that could be adapted to RPG purposes. Base price, plus so much if on a corner, plus so much for each additional bedroom or square foot, plus so much for amenities, plus a factor for close to transport / shopping, plus so much for additional garage space, and so on.

The reality is very different. The actual practice boils down to looking at how much similar homes sold for in the area in the past, adding a fudge factor for how out of date those prices were, and adding another fudge factor for how much more desirable the agent thought they could make the home sound to a seller. And, if they managed to sell it for the asking price, all that happened was that a new baseline was added. Setting the price was a combination of marketing nous and instinct as much as it was a pseudo-scientific practice of increasing prices until the market squealed ‘enough!’

That won’t translate very well to an RPG system. But it gave me a huge amount of freedom when it came to developing such a system.

I started with a web search, which told me that for Australian homes valued at between AU$500K and AU$1m (most urban real estate in the cities), additional bedrooms that increased the total floor space added 50K to 80K each in 2020. I also learned that additional bedrooms pre-pandemic could add as much as 95K (2017 AUD) but that being under-bathroomed could cost as much as 205K (2017 AUD) on the final value – and that this loss would increase with additional bedrooms, by increasing the paucity of bathrooms.

I also learned that in pricey suburbs, the value of an extra bedroom could be as much as +160K to +500K (2017 AUD) but that this rate would decline with each additional bedroom unless the total land available also increased disproportionately. For a given block of land, there was an optimum dwelling size which naturally subdivided into an optimum bedroom-and-bathroom count after common areas were subtracted from the floor plans.

I was momentarily tempted to think of this in terms of a negative acceleration of price which aggregated (integrated) into a specific value for additional bedrooms, which then aggregated into a combined additional value for the building – but this was already way more complicated than I wanted to get.

Finally, I already knew that the 1986 US price of a three-bedroom home in the US was $80,300.

Confused? It’s quite a melange of facts and figures, from different dates and places, but it gave me the foundations that I needed.

Production: Process

Before I could translate that morass into a set of values, I needed to decide on the basic process that the system would utilize. How was I going to abstract the process of Valuing a specific property?

NB: As I started writing this section I became aware of a potential source of confusion: multiple meanings of the term “value”. So, here’s the general rule: When the word is Capitalized, it refers to the final Value of a dwelling. When it is not, it refers to the impact of a variable within the process of determining that Value.

I started by breaking general impressions of the building and its location into identifiable values which were to be multiplied together.

Both factors would then be multiplied by another factor that would rate whether or not the home came with more land or less than would normally be expected.

This approach generalized a huge number of variables into a single impact on the value, and meant that I could approach the overall problem from the point of view of valuing a ‘generic standard home’.

Because it was what I had statistics for, I chose a three-bedroom home as being the standard. In a third table, I could apply a fixed ‘basic adjustment’ for the number of bedrooms – a different value for the minimum and maximum Base Value.

These two values could be combined with a random value to select where in the resulting range a specific home would be located. Because I wanted something that looked basically like a dumbbell curve (values crowding toward the center or median value, with more extreme valuations – high or low – less likely), I chose 3d6.

Initially, that was where the process was going to end, but I knew that there were two additional problems that I had to account for.

On day 2, after I had started work on actually putting numbers to all these variations and generating the tables, I figured out how to solve three major holes in the process.

The first was a value multiplier for the size of the urban community around the dwelling. The standard was set to “town’ (without actually defining what a ‘town’ was, permitting that to default to the usual usage in a given campaign setting). This separated out farm houses from those in a large city.

The second was how to factor in the value of any business that was attached – whether that was simply using the dwelling as a boarding house or hotel, or a built-in shopfront (presumably with accommodations on upper floors or out the back), or whatever.

And the third was a fudge-factor to be applied to the die roll to reflect high sentimental value or especially skilled salespeople.

With those decisions, the basic methodology that would be used to determine the value of a specific property was complete.

I’ve glossed over as many specifics as possible because I didn’t know what those specifics would be, on the first day – I needed to establish the methodology so that I could work out what those values were to be.

I also used my game (and house rules) design experience to estimate how many values in each variable I could afford to use such that the resulting tables would be of manageable size.

The final design consideration was that the resulting tables would NOT be designed to be suitable for printing. That meant that for ‘the big tables’ I could put all sixteen 3d6 results, and preliminary calculations, across a page that could be improbably wide; I was more concerned with what would be legible when viewed on a screen, specifically, my screen.

If I hadn’t made that choice, I would have had to mess around with non-printing columns for those preliminary calculations and each size dwelling (by number of bedrooms) would have been three tables on separate pages. This would have been a lot more work, and I didn’t have that much time to spare.

Production: Conversions

The entire project was geared toward satisfying my campaign needs. I make no apologies for that. The in-game date is 1986, and the location is the US – specifically, the state of Arkansas (for plot reasons). The system would be naturally adaptable to other times and locations, but that was what I needed it to simulate as a default.

This is a problem that has been addressed for the pulp campaign (set in the mid-1930s, but with financial values matching those of 1930 specifically) on many past occasions.

For consistency, conversions are always handled in a specific way in my games:

Modern to Then:

  1. Temporal correction first
  2. Regional correction second

Then to Modern:

  1. Regional correction first
  2. Temporal correction second

Only if I can’t find the appropriate historical conversion information will some compromise be used.

In this case, I was converting numbers from 2021, 2020 and 2017 AUD, to 1986 USD. In the Pulp campaign, it would be to USD from 1930.

I was able to access and download an economic report in spreadsheet format showing the historic conversion rates from Australian Dollars to US Dollars. Historically, this has varied somewhere between 1 AUD buying somewhere close to 50 cents US and a high close to parity (1 dollar US). In more recent times, somewhere between 70 cents and 86 cents US has applied; but by comparing the historical data, I found that at the time in question, the rate was almost 2/3 of a dollar – 66.01 cents, to be exact.

If I had not found this information, I would have had to convert at some assumed rate based on contemporary economics that would have been quite incorrect. If I had used 72 cents, for example, my converted values for properties would have been only 91.667% of what they should have been – an undervaluing of almost 10%. If I had used 75 cents, the error would have been about 12%. These errors might not have been either visible or significant – but why take the chance?

Next, I employed one of my bookmarked resources – the inflation calculator at Calculator.net, which has proven massively useful on a number of occasions, because it works the inflation adjustment in either direction (unlike some others). I fed in $100 US in 2021 and converted it to 1986 dollars, which gave me the percentage to multiply the relevant conversions by. Repeat for the 2020 and 2017 conversions.

For the record:

  • 2021 to 1986: × 0.41524
  • 2020 to 1986: × 0.42039
  • 2017 to 1986: × 0.44778

(divide to go backwards, i.e. from 1986 to 2021 = $ / 0.41524. So a home computer costing $3,499 back then is the equivalent of one costing $8, 426.45 in today’s money. Of course, if you were to buy something of equivalent capabilities, you would be talking a LOT less than that – maybe $84, maybe $8.40!)

With the key values converted to the contemporary target currency, I was able to do a lot of math that I’m not going to bore you with, and start populating my tables, simplifying and generalizing as I went.

Production: Execution

There’s a trick that I learned a long time ago in physics: if you have two independent variables, and you need to assess them, control one axis at a fixed standard value and vary the other one.

    Generalization, Size, Quality, & Location (Table 1)

    For table 1, I set the ‘typical’ value to 1 for both variables. Using the old maxim of location being more important than size and quality (or anything else), I set the location values to

    • Very Desirable = 4
    • Desirable = 2
    • Typical = 1 (by definition)
    • Undesirable = 0.6
    • Very Undesirable = 0.2

    …and the size and quality values to

    • Very Desirable = 2
    • Desirable = 1.5
    • Typical = 1 (by definition)
    • Undesirable = 0.8
    • Very Undesirable = 0.4

    Simple multiplication let me fill out the rest of table 1.

    Generalization, Accompanying land (Table 2)

    I then listed all the results as one axis of table 2 (labeled the “SQ,Lo Value”), and eliminated redundant results. Once again, the “1” result (since 1 × 1 = 1) let me assess the relative Land values as

    • Lots (more) = 1.5
    • More = 1.2
    • Normal = 1 (by definition)
    • Less = 0.8

    I was going to include a 5th option, “none” (with a value of 0.5) but… well, I’ll get back to that.

    Again, simple multiplication of these values by the SQ,Lo value let me fill out table 2. I noted that the peak was x12, while the minimum was × 0.06.

    Dwelling Price Range

    Next. I turned my attention to table 3, which was to provide the other set of inputs into table 4.

    I had already set the base value as being that of a three-bedroom house. That went with the known median value of $80,300. All else being equal, I decided that a variation of plus-or-minus $10,000 sounded about right. That gave me a base minimum price of $70,300 and a maximum of $90,300. I could have made it more, or less – I considered both – but most of the volatility in price was going to come from the combined general assessment that I had made and the ‘market forces’ factor to come.

    • 0.06 × 10,000 = $600 – so the bottom end of the market came out with a ‘plus or minus’ of just $600.
    • 12 × 10,000 = $120,000 – so the top end of the market came out with a ‘plus or minus’ of $120,000.

    Again, these values seemed about right, so I didn’t change the $20-000 range.

    Copying those values filled out the entire “base min” and “base max” column. I then turned my attention to the adjustment to these low and high values for additional bedrooms, all else being equal.

    Averaging the various values my research had provided (after conversion) and rounding to a convenient number gave me an adjustment of $12.500 per additional bedroom to the minimum and $20,800 to the maximum price. Rather than fuss around with diminishing impact on the price as the number of rooms increased, I simply multiplied the difference in bedrooms from the three-bedroom standard by these values to fill out the d1 and d2 columns, respectively. There was more than enough variability coming from other factors to ‘contain’ this error, I had decided.

    Adding the resulting d1 to the base min gave the adjusted minimum price for a house of that number of bedrooms; the d2 and base max gave the adjusted maximum price.

    Price by # Bedrooms and SQLoLa Value (Table 4, one page per Bedroom count)

    This is the real meat of the system. One table for each count of bedrooms, the combined result of the general assessments on the left, and a random roll from 3 to 18 across the top to yield a base value for a specific dwelling.

    • I had to start by filling out the left-hand column, again eliminating all the redundant entries.

    I mentioned the 5th option on the “Land” variable (for “none”) earlier – I found that there were too many results if I did so to get them onto a single usable table. A compromise for the sake of practicality of use had to be made; I found another way around that for the purchase of Units, Lofts, Townhouses, etc, which I’ll cover in a separate section below

    Next, there are the four columns in green, which are intermediate steps used only to generate the rest of the table.

    • The “MIN” value is the “adjusted minimum price” for a dwelling of this number of bedrooms (from table 3) multiplied by the SQLoLa value (from table 2).
    • The “MAX” value is the “adjusted maximum price” for a dwelling of this number of bedrooms (from table 3) multiplied by the SQLoLa value (from table 2).
    • The “RANGE” value is the difference between these.
    • The “Range/18” value is the RANGE divided by 18 – so that I can simply multiply by the die roll result.

    Which brought me to the heart of the results – multiplying each row’s “range/18” value by successive values and adding the “MIN” result for each row.

    Rounding The Corner

    When you look at the main part of the results tables, you will notice that – in addition to the background “banding” – some values are in black and some in blue. In fact, they all started as red (to indicate that I hadn’t yet adjusted the rounding) and the colors were applied as those adjustments were made.

    This part of the process was inconsistent across the entire process. In fact, I changed rounding and thresholds no less than five times. No, six. Sometimes I went back and corrected, sometimes I made the adjustment only from that table of results forward. Complicating that is that it was quite late in the process that I decided to put the “one bedroom” results into the table at all – originally, I started at “2 bedroom”.

    The rounding to apply depended on the result Value. The pattern that I ended up with is:

    • $2.25m+ = round to nearest $50 000 = blue
    • $1m to $2.249m = round to nearest $25 000 = black
    • $500K to $999K = round to nearest $10 000 = blue
    • $200K to $499K = round to nearest $1 000 = black
    • $100K to $199K = round to nearest $500 = blue
    • $30K to $99K = round to nearest $100 = black
    • $10K to $29K = round to nearest $50 = blue
    • less than $10K = round to nearest $10 = black

    At one point, there was an additional tier at the top,

    • $3m+ = round to nearest $100 000 = black

    and the tier below that was from $2.25m to $2.999m, but the band of results didn’t seem wide enough to justify it, so it got scrapped.

    Size Of Urban Community (Table 5)

    It may have been possible to incorporate this into the first three tables, but by the time I realized that it was a thing, I had already generated more than 2/3 of the entries for table 3 and they would all have to be redone – and table size was a consideration.

    Thus, the decision was made to stick it on as an afterthought. There are seven entries:

    • None = 0.5
    • Hamlet = 0.7
    • Village = 0.85
    • Town = 1 (defined as such)
    • Small City = 1.5
    • Lge City = 2
    • Metropolis = 2.5

    In retrospect, though, it was a brilliant move to have this as an afterthought because these are perhaps the most contentious values to assign; the population levels are poorly-defined at best and subjective at worst, and could well be different in different campaign settings; and the average impact on pricing is quite dependent on those population levels and their consequent impact on housing prices.

    Sidebar: Homes without land

    For the sale of units, you can actually consider these to come ‘with a pro-rata share of the land’ on which the block of units sits. Some new blocks of units near me were also sold with an option on some of the shop space on the ground floor of the block. Which means that you can start with the “less land” option on table 2. However, that would only get you a value for a first floor or ground floor unit; every story further up, the value drops. It’s about 8% a floor (but I would use 10% for simplicity) up to 40% and then about half that thereafter, until you get down to about 60% discount.

    • Ground Floor = 100% of the result using the ‘less land’ value
    • First Floor = same as Ground Floor
    • 2nd Floor = 90% of the result using the ‘less land’ value
    • 3rd Floor = 80% of the result using the ‘less land’ value
    • 4th Floor = 70% of the result using the ‘less land’ value
    • 5th Floor = 60% of the result using the ‘less land’ value
    • 6th Floor = 55% of the result using the ‘less land’ value
    • 7th Floor = 50% of the result using the ‘less land’ value
    • 8th Floor = 45% of the result using the ‘less land’ value
    • 9th Floor = 40% of the result using the ‘less land’ value
    • 10th Floor and above = same as the 9th floor

    Townhouses and anything else that doesn’t come with land, but is actually a purchase and not a lease, use the “Normal” land value and then halve the result from table 5.

    NB: This part of the process is so new that it isn’t even shown in the instructions that form part of the PDF / spreadsheets! Which is why I’ve labeled it as a sidebar and put the whole thing in bold.

    Business Included

    This is actually three small tables and a procedure. It does make a couple of important assumptions that I took into account when formulating this part of the process.

    The notion is that the potential profitability of the business determines its value, but it divides that profitability up into three different time periods – the first year (immediate profitability), the medium term (two to five years), and the long term (more than 5 years).

    It’s in assessing those profitability that the assumptions come into play – the table uses the profitability IF these assumptions are satisfied. If you do something other than what is assumed, you may achieve different results in terms of business success!

    The first assumption is that the usage of the rooms is as the new owners intend at the time of purchase. That means that if they intend to repurpose one of the bedrooms towards the business (by making it a home office, for example), the valuation of the overall package (business plus dwelling) should take that into account.

    The second assumption only applies to the medium- and long-term assessments, and is that any necessary investment will be made as required – which obviously reduces profitability by spending money.

    Each result on the table assesses the profitability during one of the time-frames; the assessments are exactly the same (Very Poor, Poor, Break-even, Good, and Very Good), but these are not equal in value; the long-term tends to dominate, for good or ill, and the medium-term is more important than the short-term.

    The process is to make all three assessments, and multiply the three resulting values together. If the total result is less than 1, round the valuation factor up to 1 – as it says on the table, you can always simply close the business and consider the premises simply as a dwelling. You then multiply the dwelling value by the result to get the net price.

    The values have been carefully selected to reflect the real-life experience of buying and selling a commercial operation as I understand it – I’m not an expert on this, but pay attention to things when others discuss such matters because you never know when the information will be useful.

    It’s worth checking out a couple of the possible combinations, as I did before finalizing these values.

    • Short-term break-even, medium-term poor, long-term good – describes a situation in which investment in the medium term yields a good longer-term outcome. 1 × 0.8 × 1.5 = 1.2.
    • All three break-even – describes a situation in which the business itself is barely holding its head above water but is either good for a retiree, or well-positioned should market conditions change, or owns intellectual property that in itself is valuable. 1 × 0.9 × 0.7 = 0.63, rounds up to 1. The business adds nothing to the overall value of the property.
    • Very Good short-term, Very poor medium-term, very good long-term – the business is profitable at the moment but requires hefty investment in the infrastructure in the medium-term. If you survive doing so, the prospects are excellent for the long-term. 1.4 × 0.5 × 2 = 1.4.
    • Same situation but the choice is to run the business into the ground while you can, because the required investment cannot be made for some reason – out of date equipment or whatever. That means that medium-term profitability is good, but long term is very poor. 1.4 × 1.3 × 0.25 = 0.455, rounds up to 1. The business doesn’t add to the value of the property.
    • Finally, let’s look at the best possible combination – very good profitability in all three time-frames (clearly indicating some other reason for the sale than profits – retiring, or ill-health, or the owner died, or something). 1.4 × 1.7 × 2 = 4.76. The attached business adds almost 400% to the cost of the building.

    It is worth noting, also, that the size of the urban community doesn’t explicitly impact the value of a business, but some businesses require a community of a certain size in order for them to be viable commercial propositions. A 500-bed luxury resort in the middle of nowhere is unlikely to do a roaring trade; a gas station in the same location or a smaller, cheaper, hotel might do quite well. But that means that the current location is implicitly bound up in the assessment of the profitability. A business might be unprofitable now, break-even in the short-term, but likely to thrive in the longer term, simply because the community is growing dramatically and the demand for whatever it is that the business offers is going to increase – eventually.

Usage

I’m going to be fairly brief in this section because I have an example coming up in the next section that will make everything a lot clearer.

At the same time, I want to take the time to add a couple of notes that aren’t entirely clear. My starting point for this section will be the instructions from the PDF, but with the added notes tacked on where relevant; think of them as the “designer’s notes”.

Let’s start here: this is a flowchart describing the process of using the system:

It should not escape attention that the basic process for usage is the same sequence as construction of the system, with one exception (faded on the flowchart): Table 3 is not required unless you are extending the table. And there will be times when you want to do so; that’s why there is a later section devoted to the procedure for doing so. But let’s avoid getting too far ahead of ourselves.

    1. Assess Size & Quality (yellow table top left)

    Size quite clearly has little or no resemblance to the number of bedrooms. In fact, you can almost consider it to be the size of everything except the bedrooms – almost, but not quite. Don’t over-think these assessments.

    2. Assess Location (yellow table top left)

    I usually have the advantage of context – I’ll already have some idea of the population of the location, it’s economy, it’s history, and any unsavory attitudes on the part of the locals – and any good neighbors, too. If you have to, you can live without this information, but you’ll find life a lot easier with it – even if you have to invent it out of whole cloth.

    3. Cross-reference to get SQLo Value
    4. Locate result on Pink Table, far left

    Look down the left-hand column of the pink table and find the result.

    5. Assess land that comes with property (pink table far left)

    This is another general impression but circumstances matter. If we’re talking a farmhouse, the land that is ‘normal’ might be considerable (50 acres or more – 20 hectares for metric users) but in a city it might be 1/4 of an acre. Similarly, the amount needed to qualify for “Lots more than usual” (as opposed to just more than usual) would also change with the circumstances.

    If you want to get technical, it’s actually the value of the land that is being assessed, but without putting a dollar value to it – 5000 acres of desert might be needed to get to “Lots” or 500 acres of farmland – but a mansion on 6 acres of land in a city would probably qualify.

    Use your descriptive language as the foundation, and that will generally take all these technicalities into consideration without your even thinking about it..

    6. Cross reference land with SQLo to get SQLoLa value (pink table, far left)

    It’s probably worth jotting this down on a scrap of note paper.

    7. Select Blue Table appropriate to number of bedrooms

    In the top left of each table you will find the number of bedrooms. It’s worth actually taking a quick glance at table three (to the right of table 1) just to be sure that there’s one of the appropriate size.

    If you’re working off an image, you might have to estimate the number of bedrooms. This is where it’s appropriate to use the extra time you saved by being so quick and instinctive in the earlier steps.

    8. Locate matching SQLoLa Value

    Once you’ve found the right table, go down the left hand column to find the value from table two that matches.

    9. Roll 3d6 (Optional: 3d6 & d4, see below)
        9a. For high sentimental value properties or especially skilled salespeople, use 3d6 & d4, as follows:
        a. Roll all four dice
        b. Select the d6 with the lowest showing value.
        c. Compare with the result showing on the d4.
        d. Discard whichever of the two is the lower.
        e. Read the total of the remaining dice as though they were 3d6.

    It works out that exactly half the time, this results in an improvement to the total of at least 1. More than 10.5% of the time, you will get an improvement of three! This makes a significant improvement to the value of the dwelling.

    10. Cross-reference to get property base asking price

    Find the “3d6” result column that the roll has indicated and follow it down until you get to the row that contains the SQLoLa value that you determined earlier. Or track across from that value until you find the indicated column.

    Jot down the resulting value.

    (Okay, it’s bound to come up and this my last chance to explain it. SQLoLa stands for “Size, Quality, Location, Land”)

    11. Assess Size Of Urban Community (orange table, page 1)

    This is where the context information that I mentioned previously becomes really essential. Remember that these ratings are according to the standards of your game setting. A “Town” in 1986 (or 2021 for that matter) is something quite different from the meaning of 1686, which is different to the meaning of 1286.

    12. Multiply Community Size Factor by base asking price

    Multiply the factor that you get from the orange table by the price that you have written down. Write down the result if it’s different, and cross out the old price so that you don’t get confused.

    If there is no business, skip steps 13 and 14.

    13. If there is a business attached to the property, assess Business Factor (Purple table, page 1)

    I’ve already discussed how this works, earlier in the article.

    14. Multiply current asking price by business factor (if any)

    Multiply the business factor by the current price you have written down. If it’s different, write down the new answer and cross out the old one.

    15. Result is the FINAL ASKING PRICE. Most sales can be settled for 90% of this.

    I’ve put the meaning of the result in capitals because it’s important. If a PC negotiates, they may get the acceptable price down to 90, 85, even 80% of the asking price – but if they fail, it could go up 10, 15, even 20%.

    It’s never enough simply to roll unless its a private sale. Realtors have enough experience and expertise that the majority of ploys won’t work on them. This should be roleplayed and the GM should determine from the strength of the roleplay what result this represents – if he’s feeling generous, it might be a blended roll (half rolled and half from roleplay).

    The more of a back-and-forth you can make this, the better it will be. It’s very rare in real life, when negotiating a sale of this magnitude, for someone to say “$X and that’s my final offer, take it or leave it” – at least right off the bat, it is. You might get to that point after going back-and-forth for a while. Remember that the salesman’s commission is usually a percentage of the sale price, so they have a vested interest in pushing that price up.

    16. If Realtor’s fees etc are relevant, increase price 20%.

    I’ve said 20%, but in some cases it may be only 10%, in others, 25%. This often depends on a whole range of factors – how long the property has been on the market, how many times the Realtor has tried without success to sell it (costing him time and effort, and making him more likely to accept a lower commission just to get it off his books), his mood on the day, pressure from the people he’s representing, whether or not he thinks the buyers will fit in around here, and many more. Again, use your gut instincts to assess the situation and translate into an appropriate commission percentage.

    In most (but not necessarily all) jurisdictions, this amount gets added on the top of the agreed price, something that has caught a lot of buyers out in the past. If that’s not the case, you may need to pre-load this into the asking price before negotiations begin.

    There may also be land taxes or sales taxes (or whatever) to take into account. These are generally assumed to be factored into the asking price, but if for some reason you don’t think that they would be, you should explicitly define how much they are and add that to the asking price as well.

    In theory, the Realtor’s fees would exclude any such increase, but that’s too much effort for not enough gain in accuracy – the variables involved are more than enough to cover this minor discrepancy.

    There may also be X-factors that I haven’t thought of, and that only affect this specific property. If a place has a reputation for being haunted, or the land is contaminated, or it has its own private airstrip, or anything else you can think of, be sure to add something to the asking price to cover that additional value.

Example

Here’s a picture of exactly the sort of dwelling that the PCs are looking for.

Image by Paul Brennan from Pixabay

So let’s evaluate this place and how much – in 1986 – the system thinks it would cost to buy.

    1. Assess Size & Quality (yellow table top left)

    This is a nice place, and a fair size. It’s not a huge mansion, but it’s clearly bigger than average. I rate is as desirable.

    2. Assess Location (yellow table top left)

    It looks like a nice location but the grass is a little dry – so better than ‘typical’ but not top of the tree. That rates it as desirable on this axis, too.

    3. Cross-reference to get SQLo Value

    The Desirable-Desirable match gives a SQLo of 3.

    4. Locate result on Pink Table, far left

    Five rows down, between the 2 and the 3.2.

    5. Assess land that comes with property (pink table far left)

    The environment looks suburban. There is another house visible at the top of the hill in the background (just barely), so there is a back yard of some size. However, not all of it necessarily belongs to this property – in the absence of a fence-line, the assumption has to be that half of the back yard belongs to this house. Now, the house is larger than most, and it has a reasonable front yard as well, so it clearly has more than is usual – but it doesn’t have a whole estate. So that leaves it in the ‘more’ category and not the “lots more’.

    6. Cross reference land with SQLo to get SQLoLa value (pink table, far left)

    The intersection of “More land” and an SQ,Lo of 3 gives an SQLoLa of 3.6.

    7. Select Blue Table appropriate to number of bedrooms

    Next, I need to estimate the number of bedrooms. Assuming that they are all on the second story, I count two bays of windows on the side and one at the front left. That’s three visible bedrooms, and it’s a safe bet that there are one or two – but probably not three – out of sight. That gives a total count of 4 or 5 bedrooms. Since there might be a guest room on the ground floor, I’ll choose the higher of the two values as most probable.

    A quick check of the blue table at the center top of page 1 confirms what I already knew – 5 bedrooms is one of the tables that I have generated. I would expect to find it on page 6 of the PDF… and there it is.

    8. Locate matching SQLoLa Value

    I’m looking for a SQLoLa value of 3.6 down the left-hand column – and about 12 rows down, there it is. It assigns a Min of $343,080 and a Max of $474,840. That’s a range of $131,760, and 1/18th of that is $7,320.00.

    9. Roll 3d6 (Optional: 3d6 & d4, see below)
        9a. For high sentimental value properties or especially skilled salespeople, use 3d6 & d4, as follows:
        a. Roll all four dice
        b. Select the d6 with the lowest showing value.
        c. Compare with the result showing on the d4.
        d. Discard whichever of the two is the lower.
        e. Read the total of the remaining dice as though they were 3d6.

    This place looks new, at least at the front – the rear looks older. I don’t see it as having a huge amount of sentimental value to the current owners; on the contrary, I suspect that they have bought it, renovated it, and are now looking to flip it for a profit and move on to another project. A fast sale would be preferable to getting the biggest bang for their buck, and any Realtor would be given instructions to that effect. So that means that the skill of any such would not be directed toward squeezing the last dollar of purchase price from the sale. Hence, no d4.

    I roll 3d6 and get a result of eight.

    10. Cross-reference to get property base asking price

    Because I’m only using half the screen to view the PDF (the document containing the article is occupying the other half) this is just a little trickier than it would be, but it’s just a matter of scrolling up, down, left, and right, until I can see both the row and column at the same time. If I had rolled higher, I might have needed to zoom out a bit, but that wasn’t quite necessary. The base asking price is $402,000.

    11. Assess Size Of Urban Community (orange table, page 1)

    The urban community – I’ve already said suburban. I have seen communities with homes of this type in towns and small cities; in desirable suburbs, you might also find them in large cities, but I think you would get better lawns and garden care in that case. Right away, then, I’m down to two possibilities.

    I’m leaning about 66-33 toward a small city, but I think I’ll step outside the system and compromise.

    2/3 × 1.5 = 1; 1/3 × 1 = 1/3; so the urban community size value that I’m going to use is 1.33, a town on its way to becoming a 2nd-class city.

    12. Multiply Community Size Factor by base asking price

    1.33 × $402,000 = $534,660.

    There’s no business involved, so I skip steps 13 and 14.

    15. Result is the FINAL ASKING PRICE. Most sales can be settled for 90% of this.

    The asking price is $534,660 – but the owners are going to be minded to settle for 85% of that for a fast sale, as already indicated.

    85% of that asking price is $454,461. Call it $455,000.

    16. If Realtor’s fees etc are relevant, increase price 20%.

    Agent’s fees would raise the price a bit.

    But the seller wants to sell quickly, and has promised to pay 5% of the fees out of his share if the realty can sell the house in the next few weeks. To encourage this, the Realtor will drop his percentage on the top to 10%, giving him a total of 15% of the purchase price.

    There will also be a 10% land tax, adding $45,000 to the price.

    But if the PCs bargain well, there’s a bit of wiggle room left – the 85% could drop to 80%, about $23,000, and everyone would be happy.

    Time to total things up:

    $455,000 + $45,000 = $500,000; add 10% Realtor’s fees to get $550,000. But the Realtor would probably accept $527,000 as a final settlement if the PCs bargain well.

    It seems a little on the high side to me, but not too far off the mark.

    There would be little public transport access, and there might be limited public amenities nearby – that could drop the price another $75,000 or so.

    $425,000, down to $402,000 if the PCs do well.

    That’s for a 5-bedroom, 3-bathroom house, luxurious interior, dining room, kitchen, maybe a swimming pool out back, two-car garage, sitting room, marble staircase, library, games room, fully decorated.

    And that sounds like a reasonable price to me.

Extension

What if the number of bedrooms we want isn’t one of the ones that I have pre-worked – what if we need to extend the system?

Tables 1, 2, 5, and 6 don’t need to change. We need a new row in table 3, and to then turn that into a new entry of table 4.

But there’s no need to do a full table – just to do the parts that we actually need. The existing set of tables are robust enough to cope with most challenges. Which means following the same process as described above until we can go no further.

I’m not going to spell out the parts that are covered by the example above; instead, I’ll provide a very synopsized account until we get to ‘the interesting bit’.

The basic concept: a 25-room hotel in a small community of perhaps 250 people (unless there are exigent circumstances, that 10-to-1 ratio is a reasonable rule of thumb for hotel size; in a more remote community like the one I grew up in, the ratio is larger, about 100-to-1).

  • Size & Quality: Typical.
  • Location: Undesirable (it’s not on the main thoroughfare through the community, it’s off on a side street some distance away).
  • SQ,Lo value 0.6.
  • Land: Normal.
  • SQLoLa value 0.6.
  • 25 rooms is not on table 3.

So that’s where the real work has to start – when the process itself identifies the need to extend the system. But we only need to produce data for SQLoLa of 0.6. And, in fact, we can skip ahead slightly further and consider the die roll – Sentimental value isn’t a factor, but the most skilled salesmen are more likely to get this commission, so let’s go for the d4 option.

Rolling 3d6 & a d4: 2, 4, 4, 3. The 3 replaces the 2, for a total of 11.

That means that we don’t need to do the full table, either – just the entry for that particular roll.

    Number of bedrooms

    We know the hotel has 25 rooms. But I also want to include a small two-bedroom manager’s residence on the side, raising the total to 27.

    d1 per room

    This is 12,500 per room, or 25 × 12,500 – but it’s reasonable to apply one of the neglected pieces of research and devalue the rooms somewhat at this point. Let’s use $10,000 per room.

    d1 = $10,000.

    d2 per room

    This is nominally $20,800 per room, but these are to be fairly bare-bones hotel rooms. The value has to be more than the $10,000 we used for d1, so it will be somewhere between that and the usual. Call it $12,000 a room. But we also need to note the fact that some of the facilities need to be more extensive than would be usual in a home – we’re talking a commercial kitchen, and perhaps a restaurant / dining room, and maybe a more substantial entertainment area, plus a spa, a reception, and a gymnasium. How much would those cost to build? $250,000? $350,000? Let’s use the latter, and load a one-25th share of that onto the d2 value (the two bedrooms for the manager’s residence should get the full standard value, which is why the 25 is still the appropriate divisor).

    $350,000 / 25 = $14,000.

    $12,000 + $14,000 = $26,000.

    d2 = (25 × $26,000 + 2 × $20,800) / 27 = ($650,000 + $41,600) / 27 = $691,600 / 27 = $25615 (rounding for convenience).

    Adj Min

    The base min and base max are unchanged at $70,300 and $90,300, respectively. So we can move straight onto the Adjusted values.

    Adj Min = $70,300 + 27 × $10,000 = $70,300 + $270,000 = $340,300.

    Adj Max

    Adj Max = $90,300 + 27 × $25615 = $90,300 + $691,605 = $781,905.

That completes the entry that’s “missing” from table 3. We can now move on to table 4, and calculate the Green Column values.

    MIN

    Our SQLoLa value is 0.6.

    MIN = AdjMin × SQLoLa = $340,300 × 0.6 = $204,180.

    MAX

    MAX = AdjMax × SQLoLa = $781,905 × 0.6 = $469,143.

    RANGE

    Range = MAX – MIN = $469,143 – $204,180 = $264,963.

    Range/18

    Range/18 = $264,963 / 18 = $14,720.17 (to two decimal places).

Which takes us to the main part of the table. We only need the value for a roll of 11.

    Base Asking Price

    Base Asking Price = MIN + Roll × (Range/18) = $204,180 + 11 × $14,720.17 = $204,180 + $161,921.87 = $366,101.87

    Rounding

    In the 200K-499K range, round to the nearest $1000.

    Rounded Base Asking Price = $366,000.

Until we get to valuing this hotel as a business, the rest of this process is just like the previous example, so it’s back to the summarized-synopsis format for a bit:

  • Size of the community – I grew up in a town of 2000 people, but in US terms it would be a second-class city. Most of the surrounding communities were considerably smaller, so I’ve seen several examples on which to judge this. To me, a hamlet has less than 100 urban residents, and maybe 400 all told including local farmers. So, a community of 250 urban residents is somewhere in between that and a town – by definition, a village. But that’s in real life – in a fantasy setting, urban populations might be smaller, and this might be a fully-fledged town. But that’s getting a bit ahead of myself in terms of this article, so let’s stay with the Village assessment.
  • That yields a community value of 0.85.
  • So the adjusted asking price = 0.85 × $366,000 = $311,100.

Which brings us to assessing the viability of this operation as a business. We didn’t have to do that in the previous example, so let’s examine these steps more thoroughly.

    Short-term profitability

    If this community is on a major interstate highway, or near a tourist attraction, then its size might be reasonable relative to the community population. Under any other circumstance, it’s probably too large by about half or even two-thirds.

    That unresolved question comes into sharp focus when we start assessing the profitability of the business. Since I’m making this example up, more-or-less as I go, any of these scenarios can be valid – but, to be honest, anyone who contemplated building a hotel of this size without at least one of those traffic-generating incentives, especially in a relatively out-of-the-way location, would be laughed out of the bank whose money they wanted to use to finance the construction.

    It doesn’t matter which market advantage the business enjoys – this might be a regional hub for a rail network, and so need to provide accommodations for railroad staff all year round, or their bread-and-butter might be tourism, or they might have some other trick up their sleeves; the specifics aren’t important, what matters is that the 10-to-1 rooms to population ratio is justified by this factor.

    That means that the short-term profitability is going to be good or very good. Unable to pick between those choices on this limited information, I’ll split the difference and assign a 1.3 short-term profitability value.

    Medium-term profitability

    There are two possible scenarios: either increasing maintenance costs will start eating into profits in the medium term, or they won’t.

    That intersects with another pair of scenarios: either the hotel will have been a big success, attracting one or more rival operations, or it will have been a moderate success, and be hostage to changing market conditions. Little stays the same forever, and if they are a hostage to the fortunes of some entity outside their control, sooner or later, that status will turn around and bite them.

    There is every reason to suspect that medium-term profitability will drop one-to-two steps relative to the short-term starting point. If the initial profitability was Good, that yields Poor or Break Even; if it was very good, it downgrades it to Good or Break-Even.

    At first, it might seem that the one value in common to both – Break-Even – should be chosen, but not so fast! If initial profitability was only Good, one of the major downward pressures on profitability (rivals) goes away, making Break-Even more likely, and possibly even keeping things in the Good column. If the initial profitability was Very Good, there’s more downward pressure – but the adjustment happens from a higher starting point, so Break-Even or Good are the likely results.

    So this time, I will split the difference between Break-Even and Good, and assign a medium-term value of 1.1.

    Long-term profitability

    The longer view is not so rosy. Those pressures on profitability will only increase with time, and eventually the inevitable will happen – rivals or market collapse. Unless something can be done in the way of generating a second string to their marketing bow, the long-term profitability prospects are Poor.

    If the present owners, or the town, have a plan, it doesn’t matter whether or not the PCs think it will work; it will indicate that something can be put in place to overcome the problem, elevating the long-term forecast to Break-Even at the very least. How much better than that it might be does depend on the success of whatever plans to confront the problem are executed, though.

    It’s possible that by luring a new major employer to the region, or developing a new industry to support the community, the outlook long-term could even be Good.

    So this time I’m going to split the difference between Poor and Good, and assign the long-term value to 1.

    Combining Values

    1.3 × 1.1 × 1 = 1.43.

    This is more than 1, so there is no rounding required.

    Applying The Business Valuation

    1.43 × Asking Price = 1.43 × $311,100 = $444,873.

With the profitability of the Hotel factored in, it has become clear why the current owners are probably wanting to sell – the business will never be as attractive a proposition as it is right now. This is undoubtedly the best time to sell.

This isn’t just a commitment to buying a business – it’s a commitment to local government and local politics, to becoming an involved and engaged civic leader. This is a defining point in the campaign, in other words.

If the GM wants to make this an attractive option because he can see interesting plotlines arising from it, its at this point that he can intervene to do so. If he thinks this will get in the way of what he wants to achieve in the campaign, he can now make choices that will discourage the PCs.

Personally, I like situations like this because ideas are already suggesting themselves to me – suppose an unsavory weapons manufacturer were to be persuaded to establish an R&D facility in the vicinity, someone who covertly supports enemies of the PCs? Making them dependent on the local presence of an enemy offers plenty of plot scope. And I always like giving the PCs things to do outside of adventuring. So I’m more likely to want to encourage this as an option.

The rest of the process is straightforward, the same as in the previous example. Just to finish things off, let’s run through it quickly:

  • Most property purchases can be completed for 90% of the asking price. Depending on circumstances, this could drop to 85% or rise to 95%. The seller is motivated to sell now but won’t want to lose much of the profitability of the sale – so I’ll set their willingness to budge to a mere 95%. So that’s $423,000 (to the nearest thousand), instead of $445,000, or a $22,000 discount.
  • Sales Tax, Legal fees, etc, totaling 8% (the community has an interest in making the sale attractive) adds $35,600. Adding that to the $445K gives $480,600.
  • Realtor’s Fees – If the Realtor is convinced that the new owners will stick around and invest in the community, he will benefit more in the long run by taking a smaller commission this time around. If he’s nearing retirement, or isn’t convinced, or is simply too short-sighted, he’s more likely to charge big. This is where I can shade the attractiveness of the proposition for the campaign’s benefit, or make it look more discouraging. I’ll set the two choices at +7% and +18%, respectively. If the latter, the unscrupulous Realtor will also use the value after sales tax instead of the lower value that he should use. So that’s either an additional $31,150, or an additional $86,500 – grand totals of $511,750 or $567,100, with possible discount of $22,000.

Adaption

It’s very easy to adapt these results to different campaigns, eras, and genres – you simply need to work out a currency conversion.

There are a lot of factors that you can take into account – material scarcity and expense, transport costs, labor costs, automata / slave labor, economics, currency standards, and more – but really, why bother?

Pick a number that feels about right and be done with it. If you can, use some equivalent property from the game system that you are importing your results into.

For example, you might decide that 260,000 gp is the right price for buying a five-bedroom home in your fantasy campaign. Or that 1.25 million gold would buy a 25-room keep. Once you have a conversion factor, you can use the system at will just by adding a step to the end of the process.

Or, perhaps you want to use the price of a Saturn-5 and Apollo capsule as a three-bedroom house (open plan, obviously). Thus you would be able to calculate the construction costs of a 4-man variant, with four times the rockets, for a Mars mission. You could even use the link provided earlier to adjust 1969 prices to 1990 or whenever.

    Sidebar: beyond now

    It’s important, when projecting values beyond now, that you remember that inflation is a compounding event. The simplest method is to go back in time as many years as you have to go forward – it won’t be very accurate but will be so much as easier. Or you can decide what the overall inflation rate will be each year from now until whenever and raise it to the power of the number of years. Beware – small differences will accumulate to a big difference.

    For example, fifty years of 2.05% inflation is 1.0205 ^ 50 = × 2.758357.
    Fifty years of 2.1% inflation is 1.021 ^ 50 = × 2.82675.
    Multiplied by $500,000, those are $1,379,178.50 and $1,413,375 respectively – a difference of $34,196.50. And that’s an almost minuscule difference!

    It’s also an improbably low value.

    • $500,000 from 1970 is equivalent to almost $3.49 million 2020 dollars.
    • $500,000 from 1920 is equivalent to a little over $1 million 1970 dollars – or $7,461,982.65 dollars in 2020.

    Around 2.74% is closer to reality – but inflation is expected to spike as economies come out of Covid restrictions, and stay high for quite a few years because of the debts various governments have accumulated while combating the pandemic.

One more example: Let’s say that your Sci-fi game lists a freighter with a 5-man (PC) crew as costing (plucks a number out of the air) 120,000 credits. Call that a standard 5-bedroom house (Captain’s quarters, two crew quarters for 2 PCs each, and two passenger cabins). Using this system, you can work out a 1986-dollar equivalent – and that conversion factor will let you use the system to work out how much a 25000-berth colony ship will cost, if that’s something you need that the game resources don’t tell you. Or a larger space yacht. Or a 10-crew freighter, or two-man explorer.

Click the icon to download the PDF and spreadsheets (662K zip file)

All the other complexities – computer systems, ship’s weapons, spacecraft hulls, etc – melt away. If you really need to factor them in, you can do it as percentage increases relative to the base model.

Lunar Colonies, Deep Space Habitats, Manned Space Telescopes, Wooden Sailing ships, log cabins, secret underground military bases – they’re all at your notepad.

Just remember, write down any conversion factors you determine so that you don’t have to repeat the work the next time you need them.

This system won’t solve all your problems – but it will give you the tools you need to start solving them yourself. It’s one less thing that you have to worry about.


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